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[Music]
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what do companies in e-commerce
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entertainment healthcare manufacturing
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marketing finance tech and hundreds of
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other industries
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all have in common you guessed it they
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all
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use data organizations of all kinds
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need data analysts to help them improve
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their processes
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identify opportunities and trends launch
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new products
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provide great customer service and make
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thoughtful
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decisions hi i'm tony a program manager
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at google
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and a data analyst myself i'd like to
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welcome you to the google data analytics
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certificate
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now there are lots of great reasons to
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earn this certificate
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maybe you're thinking about starting a
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career in the exciting world of data
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analytics
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or maybe you're just fascinated by the
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power of data as i
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am no matter what brought you here
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you're in the right place to kick-start
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a career
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and learn industry relevant skills in
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data analytics
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but first what exactly is data
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well i like to say that data is a
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collection of facts
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this collection can include numbers
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pictures
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videos words measurements observations
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and more
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once you have data analytics puts it to
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work through analysis
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data analysis is the collection
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transformation
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and organization of data in order to
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draw conclusions
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make predictions and drive informed
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decision making
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and it doesn't stop there data evolves
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over time
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which means this analysis or analytics
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as we call
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it can give us new information
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throughout data's entire
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life cycle data is everywhere
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you use and create data every day have
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you ever read reviews of a product
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before deciding whether or not to buy it
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that's data analysis
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or maybe you wear a fitness tracker to
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count your steps
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so you can stay active throughout the
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day that's data analysis
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but you don't just use data you also
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create huge amounts of it
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every single day anytime you use your
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phone
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look up something online stream music
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shop with the credit card post on social
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media
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or use gps to map a route you're
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creating data
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our digital world and the millions of
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smart devices inside of it
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have made the amount of data available
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truly mind-blowing
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here at google we process more than
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forty 000 searches
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every second that's 3.5 billion searches
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a day
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and 1.2 trillion searches every year
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here's another way to think about it
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youtube has almost
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2 billion users if youtube users
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made up a country it would be the
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largest in the world
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all of that data is transforming the
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world around us
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the publication the economist recently
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called data
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the world's most valuable resource so
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it's easy to see why data analysts
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are so valued by their organizations and
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what exactly does a data analyst do
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put simply a data analyst is someone who
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collects
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transforms and organizes data in order
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to help make informed decisions
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besides the role itself one of the most
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exciting parts of being a data analyst
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is the number of opportunities available
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the demand for data analysts
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is greater than the number of qualified
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people to fill these job openings
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and this certificate program is a great
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first step
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in your journey to finding a job you
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love
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data analysts come from many different
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backgrounds and have all kinds of life
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experiences
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you don't need decades of work
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experience or an expensive education
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to get started many data analysts
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taught themselves the skills they needed
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to land their first job
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just like you're doing right now ok
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now let's talk more about what you're
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going to learn
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the google data analytics certificate is
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split into courses
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based on different processes for data
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analysis
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those are ask prepare
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process analyze share
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and act plan to watch these videos
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in order each one covers a new topic
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and every topic builds on what you've
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learned before
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making it easy to track your progress
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and you're in the driver's seat
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even though you might see things
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organized by weeks
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everything can be completed at your own
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pace so you
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decide how much you want to do each day
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by the end of the program you'll take
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everything you've learned
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and turn it into a project that you can
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use to show off your skills
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and while hiring managers at your job
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interviews
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now along the way you'll also hear from
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googlers
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that's what we call people who work here
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at google
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they'll give you an inside look at what
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it's like to work in our industry
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and share personal stories of how they
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got into the field
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they'll also give you some excellent
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tips on how to land your dream job
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stay tuned some of them are going to
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introduce themselves
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in just a sec so i'm angie
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i'm a program manager of engineering at
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google
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i truly believe that cleaning data is
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the heart and soul
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of data it's how you get to know your
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data its quirks its flaws
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its mysteries i love a good mystery and
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it felt like a superpower almost like i
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was a detective and i'd gone in there
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and i'd really solved something
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hi i'm alex i'm a research scientist at
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google
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i research the different impacts of
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artificial intelligence on society
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and our users so my name is lyla jones
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and
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i am a part of our cloud team i get a
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chance to lead a team of amazing
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individuals that are focused on helping
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customers get to the cloud
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hi i'm evan i'm a learning portfolio
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manager here at google
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and i have one of the coolest jobs in
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the world where i get to look at all the
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different technologies that affect
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big data and then work them into
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training courses like this one
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for students to take i'll be your
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instructor for the first course
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i'll take you through each module that
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will cover a specific topic
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in a few different ways you'll have
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videos reading materials
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quizzes hands-on activities and
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discussion prompts
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for you to chat about with other
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students in an online
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forum i'm really excited to be guiding
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you through this course
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but i'm especially excited that you've
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chosen this adventure
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lifelong learning is something that i'm
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very passionate about
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growing up when i looked around i often
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didn't see many options available to me
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it wasn't until i started getting
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serious about my education
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that i realized i had the control to
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make my own opportunities
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with education being the key that would
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open those doors
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the more i learned and the harder i
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worked the more possibilities opened up
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had i not gone after that knowledge and
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continued challenging myself
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i may not be where i am today learning
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allowed me to grow
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personally be successful visit places
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i would never have seen and meet people
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i would never have known
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and now i'm going to introduce some of
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those great people
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hello i'm ximena financial analyst i'll
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be helping you learn how to ask the
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right questions about data
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the project you work on and the problems
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you're trying to solve
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hey my name is halle analytical lead i
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am so excited to show you
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how to prepare your data so it's ready
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for analysis
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hello i'm sally measurement and
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analytical lead
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together we'll cover how to process and
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clean your data cleaning data doesn't
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require soap and water
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i'm talking about making sure your data
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is complete correct
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and relevant to the problem you're
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trying to solve
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hey i'm ayanna global insights manager
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we'll be digging into analysis you'll
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learn how to collect
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transform and organize data so that you
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can use it to discover
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useful information draw conclusions and
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make great decisions
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my name is kevin and with my experience
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as director of analytics at google i'll
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guide you through what i think is the
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most exciting part of the data analysis
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process
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plan create and present effective and
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compelling data visualizations
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hello my name is carrie i can't wait to
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tell you about all the exciting things
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you can do with the programming language
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r are you ready
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hi i'm rishi global analytics skills
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curriculum manager
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i'm going to help you bring together
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everything you learn during this program
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by creating a case study that will
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dazzle any hiring manager just like the
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capstone of a great building shows
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everyone that it's complete
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your case study will signify your own
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great achievement
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of earning a google certificate in data
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analytics
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okay are you getting excited about the
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potential of becoming a data analyst
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so much as possible with data you're
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about to enter
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a whole new world ready let's go
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[Music]
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data data data i can't make bricks
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without clay any guesses who said this
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i'll give you a hint it wasn't a famous
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tech ceo
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or a data analyst the person who said
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this
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lived long before tech companies even
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existed
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but i bet you still heard of him this
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line was said by sherlock holmes
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the famous detective created by sir
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arthur conan doyle
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what doyle meant was that holmes
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couldn't draw
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any conclusions which would be the
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bricks he mentioned
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without data or the clay
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you're probably not here to become a
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world famous detective but data is still
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the building block
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that you'll use for everything you do in
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your new data analyst career
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sherlock holmes would agree by starting
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this program
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you've shown that you and sherlock
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holmes have something in common
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you both have an interest in learning
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more that's one of the most important
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qualities that data analysts can have
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now there are a bunch of different ways
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to explore data
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but one of the great things about data
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analytics is that you can often learn
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how you want
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when you want that might mean doing your
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own research
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talking with people in the industry or
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taking online courses
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with that said welcome to your first
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course
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this is your introduction to the
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wonderful world of data analytics
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since data analytics is the science of
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data
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you use this course to begin to learn
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all about
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data data is basically a collection of
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facts or information
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and through analysis you'll learn how to
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use the data to draw conclusions
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and make predictions and decisions
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personally
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i didn't jump right into the data
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analytics field
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i thought data analysis was for computer
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engineers
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instead i started off with dreams of
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working in finance
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once i got through an internship though
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i realized
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it wasn't the career path i wanted to
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take i started to learn about financial
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planning
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and analysis and all of the work finance
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analysts were doing with data
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i realized that finance analysts are
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really just data analysts
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working in a finance department these
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analysts
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were helping to guide business decisions
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by knowing how to use data
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it was then i realized how powerful data
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is
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and i started to embrace it soon enough
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i realized i could do this data analysis
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myself
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data analytics is a big open world of
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opportunity
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there are so many areas your analysis
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skills can be applied
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and in all kinds of different ways
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if you're new to this world you'll learn
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how to identify which
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path in industry might suit your skills
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and your interests the best and for
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those of you
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who already have some experience we'll
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help you open
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doors to new and exciting opportunities
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one of the skills you'll gain from the
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program is how to follow the best
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practices that analysts use
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to help make data-driven decisions
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computers are one part of the process
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but analysts rely on so much more to
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make decisions
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that's why learning how to think
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analytically and using your other skills
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and traits on the job
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will make your work easier i know you
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already know how to make good decisions
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you chose to be here after all in this
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first course
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you'll learn more about each phase of
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the data analysis process
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ask prepare process analyze
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share and act as a data analyst
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you'll go through these steps as you use
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data to inform your decisions
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eventually you'll see how this program
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itself
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is in a way its own version of this
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process
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while i know you'll enjoy watching these
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videos your trip to the first course
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will include a whole lot more
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other videos will take the form of
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vignettes where you'll learn from data
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analytics professionals
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who are already established in their
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careers they'll offer words of wisdom
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as well as tales of their own
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experiences starting off
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on their career path you'll start your
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own data journal
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that will help you keep track of what
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you've learned throughout the course
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you'll also add your own thoughts about
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what you're learning as well
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throughout the program your read up on
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how to navigate this program
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in the world of data analytics you'll
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complete activities
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including some that will help you get in
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the mindset of a data analyst
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along the way you'll also have the
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chance to connect with your fellow
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learners
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discussion prompts will give you a
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chance to share your thoughts and at the
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same time
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see what your peers think about all that
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you're learning
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these prompts will help you build a
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community support system
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to use throughout the program alright
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enough talking
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let's get started on this exciting path
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your next step
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awaits welcome back
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at this point you've been introduced to
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the world of data analytics
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and what data analysts do you've also
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learned how this course will prepare you
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for a successful career as an analyst
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coming up
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you'll learn all the ways data can be
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used and you'll discover why data
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analysts
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are in such high demand i'm not
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exaggerating when i say
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every goal and success that my team and
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i have achieved
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couldn't have been done without data
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here at google
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all of our products are built on data
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and data driven decision making
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from concept to development to launch
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we're using data to figure out the best
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way forward
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and we're not alone countless other
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organizations also see the incredible
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value in data
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and of course the data analysts who help
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them make use of it
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so we know data opens up a lot of
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opportunities
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but to help you wrap your head around
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all the ways you can actually use data
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let's go over a few examples from
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everyday life
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you might not realize it but people
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analyze data all the time
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for instance i'm a morning person a long
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time ago i realized that i'm happier and
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more productive
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if i get to bed early and wake up early
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i came to this conclusion after noticing
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a pattern
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in my day-to-day experiences when i got
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seven hours of sleep
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and woke up at 6 30 i was the most
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successful
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so i thought about the relationship
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between this pattern and my daily life
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and i predicted that early to bed early
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to rise
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would be the right choice for me and i'm
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definitely my best self
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when i wake up bright and early i bet
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you've identified patterns and
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relationships in your life too
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maybe about your own sleep cycle or how
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you feel after eating certain foods
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or what time of day you like to work out
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all of these are great examples of real
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life patterns and relationships
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that you can use to make predictions
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about the right actions to take
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and that is a huge part of data analysis
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right there now let's put this process
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into a business setting
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you may remember from an earlier video
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that there's a ton of data out there
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and every minute of every hour of every
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day
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more data is being created businesses
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need a way to control
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all that data so they can use it to
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improve processes
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identify opportunities and trends launch
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new products
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serve customers and make thoughtful
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decisions for businesses to be on top of
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the competition
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they need to be on top of their data
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that's why these companies
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hire data analysts to control the waves
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of data they collect every day
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make sense of it and then draw
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conclusions or make predictions
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this is the process of turning data into
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insights
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and it's how analysts help businesses
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put all their data to good use
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this is actually a good way to think
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about analysis turning data into
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insights
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as a reminder the more detailed
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definition you learned earlier
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is that data analysis is the collection
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transformation
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and organization of data in order to
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draw conclusions
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make predictions and drive informed
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decision making
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so after analysts have created insights
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from data what happens
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well a lot those insights are shared
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with others
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decisions are made and businesses take
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action
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and here's where it can get really
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exciting data analytics
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can help organizations completely
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rethink something they do
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or point them in a totally new direction
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for example
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maybe data leads them to a new product
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or unique service
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or maybe it helps them find a new way to
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deliver an incredible customer
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experience
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it's these kinds of aha moments that can
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help businesses reach another level
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and that makes data analysts vital to
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any business
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now that you know more of the amazing
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ways data is being used every day
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you can see why data analysts are in
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such high demand
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we'll continue exploring how analysts
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can transform data into insights that
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lead to action
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and before you know it you'll be ready
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to help any organization
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find new and exciting ways to transform
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their data
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hi i'm cassie and i lead decision
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intelligence for google cloud
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decision intelligence is a combination
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of applied data science and the social
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and managerial sciences
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and it is all about harnessing the power
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and beauty of data
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i help google cloud and its customers
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turn their data
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into impact and make their businesses
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and the world better
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a data analyst is an explorer a
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detective and an
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artist all rolled into one
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analytics is the quest for inspiration
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you don't know what's going to inspire
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you before you explore
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before you take a look around and so
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when you begin
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you have no idea what you're gonna find
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and
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whether you're even gonna find anything
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you have to
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bravely dive into the unknown
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and discover what lies in your data
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there is
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a pervasive myth that someone who works
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in data should know the
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everything of data and i think that
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that's
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unhelpful because the universe of data
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has
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expanded and it's expanded so much
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that specialization becomes important
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it's very very difficult for one person
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to know and be
584
00:19:47,200 --> 00:19:49,200
the everything of data and so that's why
585
00:19:49,200 --> 00:19:51,120
we need these different roles
586
00:19:51,120 --> 00:19:53,440
and the advice that i give folks who are
587
00:19:53,440 --> 00:19:55,200
entering the space
588
00:19:55,200 --> 00:19:58,559
is to pick their specialization based on
589
00:19:58,559 --> 00:20:01,600
which flavor which type of impact best
590
00:20:01,600 --> 00:20:04,000
suits their personality now data science
591
00:20:04,000 --> 00:20:06,080
the discipline of making data useful
592
00:20:06,080 --> 00:20:07,919
is an umbrella term that encompasses
593
00:20:07,919 --> 00:20:10,000
three disciplines machine learning
594
00:20:10,000 --> 00:20:12,799
statistics and analytics these are
595
00:20:12,799 --> 00:20:14,559
separated by
596
00:20:14,559 --> 00:20:16,559
how many decisions you know you want to
597
00:20:16,559 --> 00:20:18,799
make before you begin with them
598
00:20:18,799 --> 00:20:21,039
so if you want to make a few important
599
00:20:21,039 --> 00:20:23,120
decisions under uncertainty
600
00:20:23,120 --> 00:20:25,440
that is statistics if you want to
601
00:20:25,440 --> 00:20:27,200
automate in other words make many many
602
00:20:27,200 --> 00:20:27,600
many
603
00:20:27,600 --> 00:20:30,720
decisions under uncertainty that
604
00:20:30,720 --> 00:20:32,880
is machine learning and ai but what if
605
00:20:32,880 --> 00:20:34,000
you don't know how many
606
00:20:34,000 --> 00:20:35,360
decisions you want to make before you
607
00:20:35,360 --> 00:20:37,679
begin what if what you're looking for
608
00:20:37,679 --> 00:20:39,840
is inspiration you want to encounter
609
00:20:39,840 --> 00:20:41,919
your unknown unknowns
610
00:20:41,919 --> 00:20:45,280
you want to understand your world that
611
00:20:45,280 --> 00:20:47,840
is analytics when you're considering
612
00:20:47,840 --> 00:20:48,799
data science
613
00:20:48,799 --> 00:20:50,480
and you're choosing which area to
614
00:20:50,480 --> 00:20:52,480
specialize in i recommend
615
00:20:52,480 --> 00:20:55,679
going with your personality which of the
616
00:20:55,679 --> 00:20:56,080
three
617
00:20:56,080 --> 00:20:58,000
excellences in data science feels like a
618
00:20:58,000 --> 00:20:59,200
better fit for you
619
00:20:59,200 --> 00:21:02,960
the excellence of statistics is rigor
620
00:21:02,960 --> 00:21:04,480
statisticians are essentially
621
00:21:04,480 --> 00:21:06,799
philosophers epistemologists
622
00:21:06,799 --> 00:21:10,159
so they are very very careful about
623
00:21:10,159 --> 00:21:12,159
protecting decision makers from coming
624
00:21:12,159 --> 00:21:14,480
to the wrong conclusion
625
00:21:14,480 --> 00:21:18,000
so if that care and rigor is
626
00:21:18,000 --> 00:21:20,080
what you are passionate about i would
627
00:21:20,080 --> 00:21:22,240
recommend statistics
628
00:21:22,240 --> 00:21:24,080
performance is the excellence of the
629
00:21:24,080 --> 00:21:26,559
machine learning and ai engineer
630
00:21:26,559 --> 00:21:28,480
you know that's the one for you if
631
00:21:28,480 --> 00:21:30,880
someone says to you i bet that you
632
00:21:30,880 --> 00:21:33,039
couldn't build an automation system
633
00:21:33,039 --> 00:21:36,240
that performs this task with 99.9999
634
00:21:36,240 --> 00:21:39,120
accuracy and your response to that is
635
00:21:39,120 --> 00:21:41,200
watch me
636
00:21:41,200 --> 00:21:44,080
how about analytics the excellence of an
637
00:21:44,080 --> 00:21:44,640
analyst
638
00:21:44,640 --> 00:21:47,840
is speed how quickly
639
00:21:47,840 --> 00:21:49,760
can you surf through vast amounts of
640
00:21:49,760 --> 00:21:51,840
data to explore it
641
00:21:51,840 --> 00:21:54,720
and discover the gems the beautiful
642
00:21:54,720 --> 00:21:56,400
potential insights
643
00:21:56,400 --> 00:21:58,559
that are worth knowing about and
644
00:21:58,559 --> 00:22:01,200
bringing to your decision makers
645
00:22:01,200 --> 00:22:04,080
are you excited by the ambiguity of
646
00:22:04,080 --> 00:22:05,840
exploration
647
00:22:05,840 --> 00:22:09,280
are you excited by the idea of
648
00:22:09,280 --> 00:22:12,480
working on a lot of different things
649
00:22:12,480 --> 00:22:14,320
looking at a lot of different data
650
00:22:14,320 --> 00:22:15,760
sources and
651
00:22:15,760 --> 00:22:17,760
thinking through vast amounts of
652
00:22:17,760 --> 00:22:19,679
information while promising
653
00:22:19,679 --> 00:22:22,159
not to snooze past the important
654
00:22:22,159 --> 00:22:24,880
potential insights
655
00:22:24,880 --> 00:22:28,240
are you okay being told here is a whole
656
00:22:28,240 --> 00:22:29,360
lot of data
657
00:22:29,360 --> 00:22:32,640
no one has looked at it before go find
658
00:22:32,640 --> 00:22:34,480
something interesting
659
00:22:34,480 --> 00:22:38,159
and do you thrive on creative open-ended
660
00:22:38,159 --> 00:22:39,440
projects
661
00:22:39,440 --> 00:22:42,640
if that's you then analytics is probably
662
00:22:42,640 --> 00:22:43,840
the best fit for you
663
00:22:43,840 --> 00:22:45,360
a piece of advice that i have for
664
00:22:45,360 --> 00:22:48,080
analysts getting started on this journey
665
00:22:48,080 --> 00:22:51,200
is it can be pretty scary to explore the
666
00:22:51,200 --> 00:22:51,919
unknown
667
00:22:51,919 --> 00:22:54,640
but i suggest letting go a little bit of
668
00:22:54,640 --> 00:22:57,840
any temptations towards perfectionism
669
00:22:57,840 --> 00:23:01,360
and instead enjoying the fun the thrill
670
00:23:01,360 --> 00:23:04,240
of exploration don't worry about right
671
00:23:04,240 --> 00:23:05,039
answers
672
00:23:05,039 --> 00:23:07,280
see how quickly you can unwrap this gift
673
00:23:07,280 --> 00:23:09,520
and find out if there is anything fun in
674
00:23:09,520 --> 00:23:10,640
there
675
00:23:10,640 --> 00:23:12,159
it's like your birthday unwrapping a
676
00:23:12,159 --> 00:23:14,080
bunch of things some of them you like
677
00:23:14,080 --> 00:23:16,559
some of them you won't but isn't it fun
678
00:23:16,559 --> 00:23:17,919
to know
679
00:23:17,919 --> 00:23:22,480
what's actually in there
680
00:23:22,480 --> 00:23:24,799
hello again you've already learned about
681
00:23:24,799 --> 00:23:26,080
being a data analyst
682
00:23:26,080 --> 00:23:28,320
and how this program will help prepare
683
00:23:28,320 --> 00:23:30,400
you for your future career
684
00:23:30,400 --> 00:23:32,400
now it's time to explore the data
685
00:23:32,400 --> 00:23:34,880
ecosystem find out where data analytics
686
00:23:34,880 --> 00:23:36,159
fits into that system
687
00:23:36,159 --> 00:23:38,640
and go over some common misconceptions
688
00:23:38,640 --> 00:23:40,000
you might run into
689
00:23:40,000 --> 00:23:42,799
in the field of data analytics to put it
690
00:23:42,799 --> 00:23:43,679
simply
691
00:23:43,679 --> 00:23:46,240
an ecosystem is a group of elements that
692
00:23:46,240 --> 00:23:48,159
interact with one another
693
00:23:48,159 --> 00:23:50,799
ecosystems can be large like the jungle
694
00:23:50,799 --> 00:23:52,640
in a tropical rainforest
695
00:23:52,640 --> 00:23:56,000
or the australian outback or tiny
696
00:23:56,000 --> 00:23:59,039
like tadpoles in a puddle or bacteria on
697
00:23:59,039 --> 00:23:59,919
your skin
698
00:23:59,919 --> 00:24:01,840
and just like the kangaroos and koala
699
00:24:01,840 --> 00:24:04,240
bears and the australian outback
700
00:24:04,240 --> 00:24:07,520
data lives inside its own ecosystem too
701
00:24:07,520 --> 00:24:10,000
data ecosystems are made up of various
702
00:24:10,000 --> 00:24:10,720
elements that
703
00:24:10,720 --> 00:24:12,640
interact with one another in order to
704
00:24:12,640 --> 00:24:14,240
produce manage
705
00:24:14,240 --> 00:24:18,640
store organize analyze and share data
706
00:24:18,640 --> 00:24:20,480
these elements include hardware and
707
00:24:20,480 --> 00:24:21,840
software tools
708
00:24:21,840 --> 00:24:24,080
and the people who use them people like
709
00:24:24,080 --> 00:24:24,960
you
710
00:24:24,960 --> 00:24:26,640
data can also be found in something
711
00:24:26,640 --> 00:24:28,000
called the cloud
712
00:24:28,000 --> 00:24:30,720
the cloud is a place to keep data online
713
00:24:30,720 --> 00:24:33,039
rather than on a computer hard drive
714
00:24:33,039 --> 00:24:34,720
so instead of storing data somewhere
715
00:24:34,720 --> 00:24:36,640
inside your organization's network
716
00:24:36,640 --> 00:24:39,279
that data is accessed over the internet
717
00:24:39,279 --> 00:24:41,360
so the cloud is just a term we use to
718
00:24:41,360 --> 00:24:43,360
describe the virtual location
719
00:24:43,360 --> 00:24:45,200
the cloud plays a big part in the data
720
00:24:45,200 --> 00:24:47,520
ecosystem and as a data analyst
721
00:24:47,520 --> 00:24:49,520
it's your job to harness the power of
722
00:24:49,520 --> 00:24:51,200
that data ecosystem
723
00:24:51,200 --> 00:24:53,039
find the right information and provide
724
00:24:53,039 --> 00:24:55,120
the team with analysis that helps them
725
00:24:55,120 --> 00:24:56,640
make smart decisions
726
00:24:56,640 --> 00:24:58,880
for example you can tap into your retail
727
00:24:58,880 --> 00:25:00,240
storage database
728
00:25:00,240 --> 00:25:02,159
which is an ecosystem filled with
729
00:25:02,159 --> 00:25:03,360
customer names
730
00:25:03,360 --> 00:25:06,000
addresses previous purchase and customer
731
00:25:06,000 --> 00:25:07,039
reviews
732
00:25:07,039 --> 00:25:08,880
as a data analyst you could use this
733
00:25:08,880 --> 00:25:10,320
information to predict
734
00:25:10,320 --> 00:25:11,919
what these customers will buy in the
735
00:25:11,919 --> 00:25:14,159
future and make sure the store
736
00:25:14,159 --> 00:25:16,080
has the products in stock when they're
737
00:25:16,080 --> 00:25:18,720
needed as another example
738
00:25:18,720 --> 00:25:21,120
let's think about a data ecosystem used
739
00:25:21,120 --> 00:25:23,279
by a human resources department
740
00:25:23,279 --> 00:25:25,760
this ecosystem would include information
741
00:25:25,760 --> 00:25:28,080
like postings from job websites
742
00:25:28,080 --> 00:25:30,400
stats on the current labor market
743
00:25:30,400 --> 00:25:31,679
employment rates
744
00:25:31,679 --> 00:25:34,000
and social media data on prospective
745
00:25:34,000 --> 00:25:34,960
employees
746
00:25:34,960 --> 00:25:36,480
a data analyst could use this
747
00:25:36,480 --> 00:25:38,720
information to help the team recruit new
748
00:25:38,720 --> 00:25:39,360
workers
749
00:25:39,360 --> 00:25:41,440
and improve employee engagement and
750
00:25:41,440 --> 00:25:43,039
retention rates
751
00:25:43,039 --> 00:25:45,120
but data ecosystems aren't just for
752
00:25:45,120 --> 00:25:46,559
stores and offices
753
00:25:46,559 --> 00:25:49,520
they work on farms too agricultural
754
00:25:49,520 --> 00:25:50,080
companies
755
00:25:50,080 --> 00:25:52,480
regularly use data ecosystems that
756
00:25:52,480 --> 00:25:53,919
include information
757
00:25:53,919 --> 00:25:55,679
including geological patterns and
758
00:25:55,679 --> 00:25:57,279
weather movements
759
00:25:57,279 --> 00:26:00,080
data analysts can use this data to help
760
00:26:00,080 --> 00:26:01,120
farmers predict
761
00:26:01,120 --> 00:26:04,240
crop yields some data analysts are even
762
00:26:04,240 --> 00:26:06,400
using data ecosystems to save
763
00:26:06,400 --> 00:26:09,520
real environmental ecosystems at the
764
00:26:09,520 --> 00:26:10,880
scripps institution
765
00:26:10,880 --> 00:26:14,080
of oceanography coral reefs all over the
766
00:26:14,080 --> 00:26:14,799
world
767
00:26:14,799 --> 00:26:17,200
are monitored digitally so they can see
768
00:26:17,200 --> 00:26:18,240
how organisms
769
00:26:18,240 --> 00:26:21,200
change over time track their growth and
770
00:26:21,200 --> 00:26:21,679
measure
771
00:26:21,679 --> 00:26:24,080
any increases or declines in individual
772
00:26:24,080 --> 00:26:25,039
colonies
773
00:26:25,039 --> 00:26:28,320
the possibilities are endless okay
774
00:26:28,320 --> 00:26:29,840
now let's talk about some common
775
00:26:29,840 --> 00:26:32,799
misconceptions you might come across
776
00:26:32,799 --> 00:26:34,640
first is the difference between data
777
00:26:34,640 --> 00:26:37,200
scientists and data analysts
778
00:26:37,200 --> 00:26:39,760
it's easy to confuse the two but what
779
00:26:39,760 --> 00:26:42,000
they do is actually very different
780
00:26:42,000 --> 00:26:44,320
data science is defined as creating new
781
00:26:44,320 --> 00:26:46,320
ways of modeling and understanding the
782
00:26:46,320 --> 00:26:47,120
unknown
783
00:26:47,120 --> 00:26:49,840
by using raw data here's a good way to
784
00:26:49,840 --> 00:26:51,200
think about it
785
00:26:51,200 --> 00:26:53,120
data scientists create new questions
786
00:26:53,120 --> 00:26:54,400
using data while
787
00:26:54,400 --> 00:26:56,159
analysts find answers to existing
788
00:26:56,159 --> 00:26:57,520
questions by creating
789
00:26:57,520 --> 00:27:00,159
insights from data sources there are
790
00:27:00,159 --> 00:27:00,559
also
791
00:27:00,559 --> 00:27:02,080
many words and phrases you'll hear
792
00:27:02,080 --> 00:27:04,080
throughout this course that are easy to
793
00:27:04,080 --> 00:27:05,440
get mixed up
794
00:27:05,440 --> 00:27:08,320
for example data analysis and data
795
00:27:08,320 --> 00:27:09,279
analytics
796
00:27:09,279 --> 00:27:11,360
sound the same but they're actually very
797
00:27:11,360 --> 00:27:12,559
different things
798
00:27:12,559 --> 00:27:15,120
let's start with analysis you've already
799
00:27:15,120 --> 00:27:16,799
learned that data analysis
800
00:27:16,799 --> 00:27:19,279
is the collection transformation and
801
00:27:19,279 --> 00:27:20,000
organization
802
00:27:20,000 --> 00:27:22,320
of data in order to draw conclusions
803
00:27:22,320 --> 00:27:23,360
make predictions
804
00:27:23,360 --> 00:27:26,159
and drive informed decision making data
805
00:27:26,159 --> 00:27:27,120
analytics
806
00:27:27,120 --> 00:27:29,360
in the simplest terms is the science of
807
00:27:29,360 --> 00:27:30,320
data
808
00:27:30,320 --> 00:27:32,080
it's a very broad concept that
809
00:27:32,080 --> 00:27:34,080
encompasses everything from the job of
810
00:27:34,080 --> 00:27:35,760
managing and using data
811
00:27:35,760 --> 00:27:37,679
to the tools and methods that data
812
00:27:37,679 --> 00:27:39,919
workers use each and every day
813
00:27:39,919 --> 00:27:42,240
so when you think about data data
814
00:27:42,240 --> 00:27:43,039
analysis
815
00:27:43,039 --> 00:27:45,760
and the data ecosystem it's important to
816
00:27:45,760 --> 00:27:46,720
understand
817
00:27:46,720 --> 00:27:49,120
that all of these things fit under the
818
00:27:49,120 --> 00:27:50,480
data analytics
819
00:27:50,480 --> 00:27:53,120
umbrella all right now that you know a
820
00:27:53,120 --> 00:27:53,840
little more
821
00:27:53,840 --> 00:27:55,600
about the data ecosystem and the
822
00:27:55,600 --> 00:27:57,679
differences between data analysis and
823
00:27:57,679 --> 00:27:59,039
data analytics
824
00:27:59,039 --> 00:28:01,520
you're ready to explore how data is used
825
00:28:01,520 --> 00:28:03,520
to make effective decisions
826
00:28:03,520 --> 00:28:05,279
you'll get to see data driven decision
827
00:28:05,279 --> 00:28:08,789
making in action
828
00:28:08,799 --> 00:28:11,120
so far you've discovered that there are
829
00:28:11,120 --> 00:28:12,559
many different ways
830
00:28:12,559 --> 00:28:15,120
data can be used in our everyday lives
831
00:28:15,120 --> 00:28:15,600
we use
832
00:28:15,600 --> 00:28:17,840
data when we wear a fitness tracker or
833
00:28:17,840 --> 00:28:19,840
read product reviews to make a purchase
834
00:28:19,840 --> 00:28:20,559
decision
835
00:28:20,559 --> 00:28:22,880
and in business we use data to learn
836
00:28:22,880 --> 00:28:24,559
more about our customers
837
00:28:24,559 --> 00:28:27,120
improve processes and help employees do
838
00:28:27,120 --> 00:28:28,880
their jobs more effectively
839
00:28:28,880 --> 00:28:31,919
but this is just the tip of the iceberg
840
00:28:31,919 --> 00:28:33,679
one of the most powerful ways you can
841
00:28:33,679 --> 00:28:35,120
put data to work
842
00:28:35,120 --> 00:28:37,279
is with data-driven decision-making
843
00:28:37,279 --> 00:28:39,520
data-driven decision-making is defined
844
00:28:39,520 --> 00:28:40,799
as using facts
845
00:28:40,799 --> 00:28:43,279
to guide business strategy organizations
846
00:28:43,279 --> 00:28:44,000
in many different
847
00:28:44,000 --> 00:28:46,880
industries are empowered to make better
848
00:28:46,880 --> 00:28:48,399
data-driven decisions
849
00:28:48,399 --> 00:28:51,440
by data analysts all the time the first
850
00:28:51,440 --> 00:28:53,600
step in data-driven decision making
851
00:28:53,600 --> 00:28:56,559
is figuring out the business need
852
00:28:56,559 --> 00:28:57,279
usually
853
00:28:57,279 --> 00:28:59,039
this is a problem that needs to be
854
00:28:59,039 --> 00:29:01,200
solved for example
855
00:29:01,200 --> 00:29:03,600
a problem could be a new company needing
856
00:29:03,600 --> 00:29:05,760
to establish better brand recognition
857
00:29:05,760 --> 00:29:08,000
so it can compete with bigger more
858
00:29:08,000 --> 00:29:09,520
well-known competitors
859
00:29:09,520 --> 00:29:11,120
or maybe an organization wants to
860
00:29:11,120 --> 00:29:13,039
improve a product and needs to figure
861
00:29:13,039 --> 00:29:15,039
out how to source parts from a more
862
00:29:15,039 --> 00:29:17,200
sustainable or ethically responsible
863
00:29:17,200 --> 00:29:18,080
supplier
864
00:29:18,080 --> 00:29:20,320
or it could be a business trying to
865
00:29:20,320 --> 00:29:21,360
solve the problem
866
00:29:21,360 --> 00:29:23,919
of unhappy employees low levels of
867
00:29:23,919 --> 00:29:25,039
engagement
868
00:29:25,039 --> 00:29:27,600
satisfaction and retention whatever the
869
00:29:27,600 --> 00:29:28,720
problem is
870
00:29:28,720 --> 00:29:31,760
once it's defined a data analyst finds
871
00:29:31,760 --> 00:29:32,559
data
872
00:29:32,559 --> 00:29:35,039
analyzes it and uses it to uncover
873
00:29:35,039 --> 00:29:35,840
trends
874
00:29:35,840 --> 00:29:38,960
patterns and relationships sometimes the
875
00:29:38,960 --> 00:29:40,480
data-driven strategy
876
00:29:40,480 --> 00:29:43,440
will build on what's worked in the past
877
00:29:43,440 --> 00:29:44,399
other times
878
00:29:44,399 --> 00:29:46,960
it can guide a business to branch out in
879
00:29:46,960 --> 00:29:48,559
a whole new direction
880
00:29:48,559 --> 00:29:50,960
let's look at a real world example think
881
00:29:50,960 --> 00:29:53,919
about a music or movie streaming service
882
00:29:53,919 --> 00:29:55,600
how do these companies know what people
883
00:29:55,600 --> 00:29:57,200
want to watch or listen to
884
00:29:57,200 --> 00:29:59,279
and how do they provide it well using
885
00:29:59,279 --> 00:30:01,279
data driven decision making
886
00:30:01,279 --> 00:30:03,039
they gather information about what their
887
00:30:03,039 --> 00:30:05,520
customers are currently listening to
888
00:30:05,520 --> 00:30:08,159
analyze it then use the insights they've
889
00:30:08,159 --> 00:30:08,799
gained
890
00:30:08,799 --> 00:30:10,880
to make suggestions for things people
891
00:30:10,880 --> 00:30:12,000
will most likely
892
00:30:12,000 --> 00:30:15,039
enjoy in the future this keeps customers
893
00:30:15,039 --> 00:30:17,679
happy and coming back for more which in
894
00:30:17,679 --> 00:30:18,640
turn means
895
00:30:18,640 --> 00:30:20,880
more revenue for the company another
896
00:30:20,880 --> 00:30:22,960
example of data-driven decision-making
897
00:30:22,960 --> 00:30:25,919
can be seen in the rise of e-commerce it
898
00:30:25,919 --> 00:30:27,279
wasn't long ago
899
00:30:27,279 --> 00:30:29,200
that most purchases were made in a
900
00:30:29,200 --> 00:30:30,720
physical store
901
00:30:30,720 --> 00:30:32,880
but the data showed people's preferences
902
00:30:32,880 --> 00:30:33,919
were changing
903
00:30:33,919 --> 00:30:36,399
so a lot of companies created entirely
904
00:30:36,399 --> 00:30:37,520
new business models
905
00:30:37,520 --> 00:30:39,919
that remove the physical store and let
906
00:30:39,919 --> 00:30:42,000
people shop right from their computers
907
00:30:42,000 --> 00:30:44,960
or mobile phones with products delivered
908
00:30:44,960 --> 00:30:46,240
right to their doorstep
909
00:30:46,240 --> 00:30:48,799
in fact data-driven decision making can
910
00:30:48,799 --> 00:30:50,240
be so powerful
911
00:30:50,240 --> 00:30:52,240
it can make entire business methods
912
00:30:52,240 --> 00:30:53,360
obsolete
913
00:30:53,360 --> 00:30:55,679
for example data help companies
914
00:30:55,679 --> 00:30:58,159
completely move away from corded phones
915
00:30:58,159 --> 00:31:00,880
and replace them with mobile phones by
916
00:31:00,880 --> 00:31:02,159
ensuring that data
917
00:31:02,159 --> 00:31:04,480
is built into every business strategy
918
00:31:04,480 --> 00:31:05,440
data analysts
919
00:31:05,440 --> 00:31:07,600
play a critical role in their company's
920
00:31:07,600 --> 00:31:08,559
success
921
00:31:08,559 --> 00:31:10,480
but it's important to note that no
922
00:31:10,480 --> 00:31:12,640
matter how valuable data driven decision
923
00:31:12,640 --> 00:31:13,120
making
924
00:31:13,120 --> 00:31:16,880
is data alone will never be as powerful
925
00:31:16,880 --> 00:31:19,760
as data combined with human experience
926
00:31:19,760 --> 00:31:20,799
observation
927
00:31:20,799 --> 00:31:24,000
and sometimes even intuition to get the
928
00:31:24,000 --> 00:31:26,159
most out of data driven decision making
929
00:31:26,159 --> 00:31:28,480
it's important to include insights from
930
00:31:28,480 --> 00:31:29,679
people who are familiar with the
931
00:31:29,679 --> 00:31:31,200
business problem
932
00:31:31,200 --> 00:31:33,279
these people are called subject matter
933
00:31:33,279 --> 00:31:35,600
experts and they have the ability to
934
00:31:35,600 --> 00:31:37,600
look at the results of data analysis
935
00:31:37,600 --> 00:31:40,320
and identify any inconsistencies make
936
00:31:40,320 --> 00:31:41,919
sense of gray areas
937
00:31:41,919 --> 00:31:44,320
and eventually validate choices being
938
00:31:44,320 --> 00:31:45,679
made
939
00:31:45,679 --> 00:31:47,919
organizations that work this way put
940
00:31:47,919 --> 00:31:49,519
data at the heart of every business
941
00:31:49,519 --> 00:31:50,720
strategy
942
00:31:50,720 --> 00:31:53,039
but also benefit from the insights of
943
00:31:53,039 --> 00:31:54,240
their people
944
00:31:54,240 --> 00:31:56,960
it's a win-win as a data analyst you
945
00:31:56,960 --> 00:31:58,640
play a key role in empowering these
946
00:31:58,640 --> 00:32:00,559
organizations to make data-driven
947
00:32:00,559 --> 00:32:01,519
decisions
948
00:32:01,519 --> 00:32:03,679
which is why it's so important for you
949
00:32:03,679 --> 00:32:05,919
to understand how data plays a part
950
00:32:05,919 --> 00:32:09,430
in the decision-making process
951
00:32:09,440 --> 00:32:11,600
we've covered a lot and i'm sure you
952
00:32:11,600 --> 00:32:13,200
have so much to think about
953
00:32:13,200 --> 00:32:16,240
already that's a good thing it means you
954
00:32:16,240 --> 00:32:17,519
started collecting data
955
00:32:17,519 --> 00:32:19,039
and you're doing your own personal
956
00:32:19,039 --> 00:32:21,760
analysis that's what it's all about
957
00:32:21,760 --> 00:32:24,159
you've built a great base already as
958
00:32:24,159 --> 00:32:25,519
this course continues
959
00:32:25,519 --> 00:32:28,159
your knowledge and data analysis skills
960
00:32:28,159 --> 00:32:30,159
will continue to grow
961
00:32:30,159 --> 00:32:31,519
once you've established a solid
962
00:32:31,519 --> 00:32:33,440
foundation you'll apply what you've
963
00:32:33,440 --> 00:32:33,919
learned
964
00:32:33,919 --> 00:32:36,080
to the rest of the program the data
965
00:32:36,080 --> 00:32:37,360
analysis process
966
00:32:37,360 --> 00:32:39,200
will help provide a framework for
967
00:32:39,200 --> 00:32:40,880
everything you do
968
00:32:40,880 --> 00:32:43,120
soon you'll take your first graded
969
00:32:43,120 --> 00:32:44,000
assessment
970
00:32:44,000 --> 00:32:45,360
it's a great way to check your
971
00:32:45,360 --> 00:32:47,440
understanding of the concepts
972
00:32:47,440 --> 00:32:50,080
and build confidence in your knowledge
973
00:32:50,080 --> 00:32:52,000
everyone learns at different speeds
974
00:32:52,000 --> 00:32:54,000
so take your time get familiar with the
975
00:32:54,000 --> 00:32:55,039
concepts
976
00:32:55,039 --> 00:32:57,360
as soon as you feel ready you can go
977
00:32:57,360 --> 00:32:58,960
ahead and get started
978
00:32:58,960 --> 00:33:02,080
keep in mind if at any point you're not
979
00:33:02,080 --> 00:33:03,679
sure about a question
980
00:33:03,679 --> 00:33:05,679
you can always review the videos and
981
00:33:05,679 --> 00:33:07,519
readings to remind yourself of the
982
00:33:07,519 --> 00:33:08,640
answer
983
00:33:08,640 --> 00:33:10,799
we're all about open book tests here
984
00:33:10,799 --> 00:33:12,399
once you've passed
985
00:33:12,399 --> 00:33:15,039
you'll be all set to move on you've got
986
00:33:15,039 --> 00:33:15,919
this
987
00:33:15,919 --> 00:33:18,000
before you know it you'll be done with
988
00:33:18,000 --> 00:33:19,360
all of the courses
989
00:33:19,360 --> 00:33:21,519
and you'll be ready to create your own
990
00:33:21,519 --> 00:33:22,640
case study
991
00:33:22,640 --> 00:33:25,200
then if it's what you want to do you'll
992
00:33:25,200 --> 00:33:26,559
start your job search
993
00:33:26,559 --> 00:33:28,720
equipped with the tools and skills that
994
00:33:28,720 --> 00:33:30,159
will wow any company
995
00:33:30,159 --> 00:33:32,960
you talk to i can't wait to see where
996
00:33:32,960 --> 00:33:33,519
you go
997
00:33:33,519 --> 00:33:36,320
with data analytics for now though give
998
00:33:36,320 --> 00:33:37,760
yourself a pat on the back
999
00:33:37,760 --> 00:33:42,389
for a job well done see you soon
1000
00:33:42,399 --> 00:33:44,799
welcome now that you have a solid
1001
00:33:44,799 --> 00:33:46,720
foundation on the basics of data
1002
00:33:46,720 --> 00:33:48,480
it's time to focus on some particular
1003
00:33:48,480 --> 00:33:50,000
skills and characteristics
1004
00:33:50,000 --> 00:33:52,000
that would be key to your future career
1005
00:33:52,000 --> 00:33:53,519
as a data analyst
1006
00:33:53,519 --> 00:33:56,399
we'll begin with five key skills move on
1007
00:33:56,399 --> 00:33:58,159
to the characteristics of analytical
1008
00:33:58,159 --> 00:33:58,880
thinking
1009
00:33:58,880 --> 00:34:00,960
and then learn how data analysts balance
1010
00:34:00,960 --> 00:34:03,200
their roles and responsibilities
1011
00:34:03,200 --> 00:34:05,360
along the way you'll also discover how
1012
00:34:05,360 --> 00:34:07,279
to tap into your own natural abilities
1013
00:34:07,279 --> 00:34:08,480
for strategy
1014
00:34:08,480 --> 00:34:11,679
technical expertise and data design
1015
00:34:11,679 --> 00:34:13,599
these are incredibly helpful skills to
1016
00:34:13,599 --> 00:34:15,440
have and you'll learn how to make them
1017
00:34:15,440 --> 00:34:17,040
even stronger
1018
00:34:17,040 --> 00:34:19,040
finally you'll be introduced to some
1019
00:34:19,040 --> 00:34:21,200
fascinating real world examples
1020
00:34:21,200 --> 00:34:23,200
of how data is influencing the lives of
1021
00:34:23,200 --> 00:34:25,280
people all around the world
1022
00:34:25,280 --> 00:34:29,030
alright let's get started
1023
00:34:29,040 --> 00:34:32,399
earlier i told you that you already have
1024
00:34:32,399 --> 00:34:34,800
analytical skills you just might not
1025
00:34:34,800 --> 00:34:35,839
know it yet
1026
00:34:35,839 --> 00:34:38,320
when learning new things sometimes
1027
00:34:38,320 --> 00:34:40,480
people overlook their own skills
1028
00:34:40,480 --> 00:34:42,560
but it's important you take the time to
1029
00:34:42,560 --> 00:34:44,000
acknowledge them
1030
00:34:44,000 --> 00:34:45,679
especially since these skills are going
1031
00:34:45,679 --> 00:34:48,079
to help you as a data analyst
1032
00:34:48,079 --> 00:34:50,079
in fact you're probably more prepared
1033
00:34:50,079 --> 00:34:51,200
than you think
1034
00:34:51,200 --> 00:34:54,480
don't believe me well let me prove it
1035
00:34:54,480 --> 00:34:56,639
let's start by defining what i'm talking
1036
00:34:56,639 --> 00:34:57,760
about here
1037
00:34:57,760 --> 00:35:00,160
analytical skills are qualities and
1038
00:35:00,160 --> 00:35:01,359
characteristics
1039
00:35:01,359 --> 00:35:03,440
associated with solving problems using
1040
00:35:03,440 --> 00:35:04,400
facts
1041
00:35:04,400 --> 00:35:06,079
there are a lot of aspects to analytical
1042
00:35:06,079 --> 00:35:07,520
skills but
1043
00:35:07,520 --> 00:35:10,400
we'll focus on five essential points
1044
00:35:10,400 --> 00:35:12,000
their curiosity
1045
00:35:12,000 --> 00:35:14,480
understanding context having technical
1046
00:35:14,480 --> 00:35:15,760
mindset
1047
00:35:15,760 --> 00:35:19,599
data design and data strategy now
1048
00:35:19,599 --> 00:35:21,680
you may be thinking i don't have these
1049
00:35:21,680 --> 00:35:22,800
kinds of skills
1050
00:35:22,800 --> 00:35:25,920
or i only have a couple of them
1051
00:35:25,920 --> 00:35:28,480
but stay with me and i bet you'll change
1052
00:35:28,480 --> 00:35:29,599
your mind
1053
00:35:29,599 --> 00:35:32,480
let's start with curiosity curiosity is
1054
00:35:32,480 --> 00:35:32,960
all about
1055
00:35:32,960 --> 00:35:35,200
wanting to learn something curious
1056
00:35:35,200 --> 00:35:37,599
people usually seek out new challenges
1057
00:35:37,599 --> 00:35:40,640
and experiences this leads to knowledge
1058
00:35:40,640 --> 00:35:42,800
the very fact that you're here with me
1059
00:35:42,800 --> 00:35:43,760
right now
1060
00:35:43,760 --> 00:35:46,960
demonstrates that you have curiosity
1061
00:35:46,960 --> 00:35:50,400
all right that was an easy one
1062
00:35:50,400 --> 00:35:53,839
now think about understanding context
1063
00:35:53,839 --> 00:35:55,760
context is the condition in which
1064
00:35:55,760 --> 00:35:57,440
something exists or happens
1065
00:35:57,440 --> 00:35:58,800
this can be a structure or an
1066
00:35:58,800 --> 00:36:00,720
environment a simple way of
1067
00:36:00,720 --> 00:36:02,079
understanding context
1068
00:36:02,079 --> 00:36:05,200
is by counting to five one
1069
00:36:05,200 --> 00:36:08,480
two three four
1070
00:36:08,480 --> 00:36:11,520
five all of those numbers exist in the
1071
00:36:11,520 --> 00:36:12,400
context
1072
00:36:12,400 --> 00:36:15,040
of one through five but what if a friend
1073
00:36:15,040 --> 00:36:15,760
of yours
1074
00:36:15,760 --> 00:36:19,520
said to you one two four five three
1075
00:36:19,520 --> 00:36:22,000
well the three would be out of context
1076
00:36:22,000 --> 00:36:23,520
simple right
1077
00:36:23,520 --> 00:36:25,760
but it can be a little tricky there's a
1078
00:36:25,760 --> 00:36:27,359
good chance that you might not even
1079
00:36:27,359 --> 00:36:27,920
notice
1080
00:36:27,920 --> 00:36:30,960
the three being out of context if you
1081
00:36:30,960 --> 00:36:33,040
aren't paying close attention
1082
00:36:33,040 --> 00:36:35,119
that's why listening and trying to
1083
00:36:35,119 --> 00:36:37,920
understand the full picture is critical
1084
00:36:37,920 --> 00:36:39,920
in your own life you put things into
1085
00:36:39,920 --> 00:36:41,760
context all the time
1086
00:36:41,760 --> 00:36:43,760
for example let's think about your
1087
00:36:43,760 --> 00:36:45,040
grocery list
1088
00:36:45,040 --> 00:36:47,680
if you group together items like flour
1089
00:36:47,680 --> 00:36:49,359
sugar and yeast
1090
00:36:49,359 --> 00:36:51,440
that's you adding context to your
1091
00:36:51,440 --> 00:36:52,640
groceries
1092
00:36:52,640 --> 00:36:54,640
this saves you time when you're at the
1093
00:36:54,640 --> 00:36:56,640
baking out at the grocery store
1094
00:36:56,640 --> 00:36:58,960
let's look at another example have you
1095
00:36:58,960 --> 00:37:00,320
ever shuffled a deck of cards and
1096
00:37:00,320 --> 00:37:01,920
noticed a joker
1097
00:37:01,920 --> 00:37:03,520
if you're playing a game that doesn't
1098
00:37:03,520 --> 00:37:05,839
include jokers identifying that card
1099
00:37:05,839 --> 00:37:06,320
means
1100
00:37:06,320 --> 00:37:08,880
you understand it's out of context
1101
00:37:08,880 --> 00:37:09,839
remove it
1102
00:37:09,839 --> 00:37:11,200
and you're much more likely to play a
1103
00:37:11,200 --> 00:37:13,760
successful game alright
1104
00:37:13,760 --> 00:37:16,320
so now we know you have both curiosity
1105
00:37:16,320 --> 00:37:18,880
and the ability to understand context
1106
00:37:18,880 --> 00:37:20,880
let's move on to the third skill a
1107
00:37:20,880 --> 00:37:23,119
technical mindset
1108
00:37:23,119 --> 00:37:25,599
a technical mindset involves the ability
1109
00:37:25,599 --> 00:37:26,800
to break things down
1110
00:37:26,800 --> 00:37:29,359
into smaller steps or pieces and work
1111
00:37:29,359 --> 00:37:29,839
with them
1112
00:37:29,839 --> 00:37:32,480
in an orderly and logical way for
1113
00:37:32,480 --> 00:37:33,359
instance
1114
00:37:33,359 --> 00:37:35,599
when paying your bills you probably
1115
00:37:35,599 --> 00:37:37,520
already break down the process into
1116
00:37:37,520 --> 00:37:39,119
smaller steps
1117
00:37:39,119 --> 00:37:41,040
maybe you start by sorting them by the
1118
00:37:41,040 --> 00:37:42,560
date they're due
1119
00:37:42,560 --> 00:37:44,960
next you might add them up and compare
1120
00:37:44,960 --> 00:37:45,680
that amount
1121
00:37:45,680 --> 00:37:48,560
to the balance in your bank account this
1122
00:37:48,560 --> 00:37:49,599
would help you see
1123
00:37:49,599 --> 00:37:52,000
if you can pay your bills now or if you
1124
00:37:52,000 --> 00:37:54,560
should wait until the next paycheck
1125
00:37:54,560 --> 00:37:57,760
finally you'd pay them when you take
1126
00:37:57,760 --> 00:38:00,079
something that seems like a single task
1127
00:38:00,079 --> 00:38:02,079
like paying your bills and break it into
1128
00:38:02,079 --> 00:38:03,280
smaller steps
1129
00:38:03,280 --> 00:38:06,079
with an orderly process that's using a
1130
00:38:06,079 --> 00:38:08,000
technical mindset
1131
00:38:08,000 --> 00:38:10,079
now let's explore the fourth part of an
1132
00:38:10,079 --> 00:38:11,680
analytical skill set
1133
00:38:11,680 --> 00:38:14,560
data design data design is how you
1134
00:38:14,560 --> 00:38:16,079
organize information
1135
00:38:16,079 --> 00:38:19,040
as a data analyst design typically has
1136
00:38:19,040 --> 00:38:19,839
to do with an
1137
00:38:19,839 --> 00:38:23,280
actual database but again the same
1138
00:38:23,280 --> 00:38:25,839
skills can easily be applied to everyday
1139
00:38:25,839 --> 00:38:26,880
life
1140
00:38:26,880 --> 00:38:29,200
for example think about the way you
1141
00:38:29,200 --> 00:38:31,520
organize the contacts in your phone
1142
00:38:31,520 --> 00:38:34,400
that's actually a type of data design
1143
00:38:34,400 --> 00:38:34,960
maybe
1144
00:38:34,960 --> 00:38:36,800
you list them by first name instead of
1145
00:38:36,800 --> 00:38:39,280
last or maybe you use email addresses
1146
00:38:39,280 --> 00:38:40,880
instead of their names
1147
00:38:40,880 --> 00:38:42,720
what you're really doing is designing a
1148
00:38:42,720 --> 00:38:45,599
clear logical list that lets you call or
1149
00:38:45,599 --> 00:38:46,640
text the contact
1150
00:38:46,640 --> 00:38:49,839
in a quick and simple way the last
1151
00:38:49,839 --> 00:38:52,240
but definitely not least the fifth and
1152
00:38:52,240 --> 00:38:54,160
final element of analytical skills is
1153
00:38:54,160 --> 00:38:55,839
data strategy
1154
00:38:55,839 --> 00:38:57,680
data strategy is the management of the
1155
00:38:57,680 --> 00:38:59,200
people processes
1156
00:38:59,200 --> 00:39:02,400
and tools used in data analysis let's
1157
00:39:02,400 --> 00:39:03,839
break that down
1158
00:39:03,839 --> 00:39:05,440
you manage people by making sure they
1159
00:39:05,440 --> 00:39:07,440
know how to use the right data
1160
00:39:07,440 --> 00:39:09,119
to find solutions to the problem you're
1161
00:39:09,119 --> 00:39:11,599
working on for processes
1162
00:39:11,599 --> 00:39:13,520
it's about making sure the path to that
1163
00:39:13,520 --> 00:39:14,640
solution is clear
1164
00:39:14,640 --> 00:39:18,000
and accessible and for tools you make
1165
00:39:18,000 --> 00:39:19,920
sure the right technology is being used
1166
00:39:19,920 --> 00:39:21,839
for the job
1167
00:39:21,839 --> 00:39:24,160
now you may be doubting my ability to
1168
00:39:24,160 --> 00:39:25,760
give you an example from real life that
1169
00:39:25,760 --> 00:39:27,520
demonstrates data strategy
1170
00:39:27,520 --> 00:39:30,400
but check this out imagine mowing a lawn
1171
00:39:30,400 --> 00:39:31,599
step one would be
1172
00:39:31,599 --> 00:39:34,079
reading the owner's manual for the mower
1173
00:39:34,079 --> 00:39:36,240
that's making sure the people involved
1174
00:39:36,240 --> 00:39:38,800
well you in this example know how to use
1175
00:39:38,800 --> 00:39:40,640
the data available
1176
00:39:40,640 --> 00:39:42,240
the manual would instruct you to put on
1177
00:39:42,240 --> 00:39:45,040
protective eyewear and closed-toed shoes
1178
00:39:45,040 --> 00:39:47,680
then it's on to step two making the
1179
00:39:47,680 --> 00:39:48,720
process
1180
00:39:48,720 --> 00:39:51,760
the path clear and accessible
1181
00:39:51,760 --> 00:39:53,359
this would involve you walking around
1182
00:39:53,359 --> 00:39:55,359
the lawn picking up large sticks or
1183
00:39:55,359 --> 00:39:57,599
rocks that might get in your way
1184
00:39:57,599 --> 00:40:00,320
finally for step three you check the
1185
00:40:00,320 --> 00:40:01,200
lawnmower
1186
00:40:01,200 --> 00:40:04,079
your tool to make sure it has enough gas
1187
00:40:04,079 --> 00:40:05,040
and oil
1188
00:40:05,040 --> 00:40:07,280
and is in working condition so the lawn
1189
00:40:07,280 --> 00:40:08,640
can be moaned safely
1190
00:40:08,640 --> 00:40:11,359
so there you have it now you know the
1191
00:40:11,359 --> 00:40:12,880
five essential skills
1192
00:40:12,880 --> 00:40:16,079
of a data analyst curiosity
1193
00:40:16,079 --> 00:40:19,119
understanding context having a technical
1194
00:40:19,119 --> 00:40:20,400
mindset
1195
00:40:20,400 --> 00:40:23,680
data design and data strategy
1196
00:40:23,680 --> 00:40:25,280
i told you that you are already an
1197
00:40:25,280 --> 00:40:26,960
analytical thinker
1198
00:40:26,960 --> 00:40:29,520
now you can start actively practicing
1199
00:40:29,520 --> 00:40:30,640
these skills
1200
00:40:30,640 --> 00:40:32,000
as you move through the rest of this
1201
00:40:32,000 --> 00:40:35,040
course curious about what's next
1202
00:40:35,040 --> 00:40:38,240
move on to the next video
1203
00:40:38,240 --> 00:40:39,760
now that you know the five essential
1204
00:40:39,760 --> 00:40:41,599
skills of a data analyst
1205
00:40:41,599 --> 00:40:43,760
you're ready to learn more about what it
1206
00:40:43,760 --> 00:40:45,760
means to think analytically
1207
00:40:45,760 --> 00:40:48,480
people don't often think about thinking
1208
00:40:48,480 --> 00:40:49,200
thinking
1209
00:40:49,200 --> 00:40:52,000
is second nature to us it just happens
1210
00:40:52,000 --> 00:40:53,280
automatically
1211
00:40:53,280 --> 00:40:55,359
but there are actually many different
1212
00:40:55,359 --> 00:40:57,520
ways to think
1213
00:40:57,520 --> 00:41:00,160
some people think creatively some think
1214
00:41:00,160 --> 00:41:01,359
critically
1215
00:41:01,359 --> 00:41:04,000
and some people think in abstract ways
1216
00:41:04,000 --> 00:41:06,400
let's talk about analytical thinking
1217
00:41:06,400 --> 00:41:08,800
analytical thinking involves identifying
1218
00:41:08,800 --> 00:41:10,319
and defining a problem
1219
00:41:10,319 --> 00:41:12,960
and then solving it by using data in an
1220
00:41:12,960 --> 00:41:13,839
organized
1221
00:41:13,839 --> 00:41:17,599
step-by-step manner so as data analysts
1222
00:41:17,599 --> 00:41:20,079
how do we think analytically well to
1223
00:41:20,079 --> 00:41:21,359
answer that question
1224
00:41:21,359 --> 00:41:23,520
we will now talk about a second set of
1225
00:41:23,520 --> 00:41:24,400
five
1226
00:41:24,400 --> 00:41:26,880
the five key aspects to analytical
1227
00:41:26,880 --> 00:41:27,920
thinking
1228
00:41:27,920 --> 00:41:31,119
they are visualization strategy problem
1229
00:41:31,119 --> 00:41:32,319
orientation
1230
00:41:32,319 --> 00:41:35,520
correlation and finally big picture and
1231
00:41:35,520 --> 00:41:37,119
detail-oriented thinking
1232
00:41:37,119 --> 00:41:39,599
let's start with visualization in data
1233
00:41:39,599 --> 00:41:40,640
analytics
1234
00:41:40,640 --> 00:41:42,560
visualization is the graphical
1235
00:41:42,560 --> 00:41:44,960
representation of information
1236
00:41:44,960 --> 00:41:47,920
some examples include graphs maps or
1237
00:41:47,920 --> 00:41:49,599
other design elements
1238
00:41:49,599 --> 00:41:51,839
visualization is important because
1239
00:41:51,839 --> 00:41:52,800
visuals
1240
00:41:52,800 --> 00:41:55,200
can help data analysts understand and
1241
00:41:55,200 --> 00:41:57,520
explain information more effectively
1242
00:41:57,520 --> 00:42:00,319
think about it like this if you are
1243
00:42:00,319 --> 00:42:02,079
trying to explain the grand canyon to
1244
00:42:02,079 --> 00:42:03,040
someone
1245
00:42:03,040 --> 00:42:04,720
using words would be much more
1246
00:42:04,720 --> 00:42:07,440
challenging than showing them a picture
1247
00:42:07,440 --> 00:42:09,200
a visualization of the grand canyon
1248
00:42:09,200 --> 00:42:11,359
would help you make your point much
1249
00:42:11,359 --> 00:42:12,160
quicker
1250
00:42:12,160 --> 00:42:14,079
now let's talk about the second part of
1251
00:42:14,079 --> 00:42:15,599
analytical thinking
1252
00:42:15,599 --> 00:42:18,240
being strategic with so much data
1253
00:42:18,240 --> 00:42:18,960
available
1254
00:42:18,960 --> 00:42:21,520
having a strategic mindset is key to
1255
00:42:21,520 --> 00:42:22,480
staying focused
1256
00:42:22,480 --> 00:42:24,960
and on track strategizing helps data
1257
00:42:24,960 --> 00:42:25,839
analysts see
1258
00:42:25,839 --> 00:42:27,920
what they want to achieve with the data
1259
00:42:27,920 --> 00:42:30,000
and how they can get there
1260
00:42:30,000 --> 00:42:32,240
strategy also helps improve the quality
1261
00:42:32,240 --> 00:42:33,200
and usefulness
1262
00:42:33,200 --> 00:42:36,240
of the data we collect by strategizing
1263
00:42:36,240 --> 00:42:39,200
we know all our data is valuable and can
1264
00:42:39,200 --> 00:42:40,960
help us accomplish our goals
1265
00:42:40,960 --> 00:42:42,640
next up on the analytical thinking
1266
00:42:42,640 --> 00:42:46,000
checklist being problem oriented
1267
00:42:46,000 --> 00:42:48,800
data analysts use a problem-oriented
1268
00:42:48,800 --> 00:42:49,280
approach
1269
00:42:49,280 --> 00:42:52,640
in order to identify describe and solve
1270
00:42:52,640 --> 00:42:53,599
problems
1271
00:42:53,599 --> 00:42:55,280
it's all about keeping the problem top
1272
00:42:55,280 --> 00:42:58,240
of mind throughout the entire project
1273
00:42:58,240 --> 00:43:01,119
for example say a data analyst is told
1274
00:43:01,119 --> 00:43:02,240
about the problem
1275
00:43:02,240 --> 00:43:04,079
of a warehouse constantly running out of
1276
00:43:04,079 --> 00:43:05,599
supplies
1277
00:43:05,599 --> 00:43:06,960
they will move forward with different
1278
00:43:06,960 --> 00:43:08,640
strategies and processes
1279
00:43:08,640 --> 00:43:11,040
but the number one goal would always be
1280
00:43:11,040 --> 00:43:12,160
solving the problem
1281
00:43:12,160 --> 00:43:15,440
of keeping inventory on the shelves data
1282
00:43:15,440 --> 00:43:16,000
analysts
1283
00:43:16,000 --> 00:43:18,640
also ask a lot of questions this helps
1284
00:43:18,640 --> 00:43:20,960
improve communication and saves time
1285
00:43:20,960 --> 00:43:23,440
while working on a solution an example
1286
00:43:23,440 --> 00:43:24,560
of that would be
1287
00:43:24,560 --> 00:43:26,160
surveying customers about their
1288
00:43:26,160 --> 00:43:28,160
experiences using a product
1289
00:43:28,160 --> 00:43:29,599
and building insights from those
1290
00:43:29,599 --> 00:43:32,000
questions to improve that product
1291
00:43:32,000 --> 00:43:33,760
this leads us to the fourth quality of
1292
00:43:33,760 --> 00:43:35,440
analytical thinking
1293
00:43:35,440 --> 00:43:37,599
being able to identify a correlation
1294
00:43:37,599 --> 00:43:40,160
between two or more pieces of data
1295
00:43:40,160 --> 00:43:43,440
a correlation is like a relationship
1296
00:43:43,440 --> 00:43:45,280
you can find all kinds of correlations
1297
00:43:45,280 --> 00:43:47,440
in data maybe it's the relationship
1298
00:43:47,440 --> 00:43:48,079
between
1299
00:43:48,079 --> 00:43:49,920
the length of your hair and the amount
1300
00:43:49,920 --> 00:43:51,520
of shampoo you need
1301
00:43:51,520 --> 00:43:53,040
or maybe you notice a correlation
1302
00:43:53,040 --> 00:43:55,200
between a rainier season
1303
00:43:55,200 --> 00:43:56,960
leading to a high number of umbrellas
1304
00:43:56,960 --> 00:43:58,480
being sold
1305
00:43:58,480 --> 00:44:00,000
but as you start identifying
1306
00:44:00,000 --> 00:44:01,520
correlations and data
1307
00:44:01,520 --> 00:44:02,880
there's one thing you always want to
1308
00:44:02,880 --> 00:44:05,520
keep in mind correlation does not
1309
00:44:05,520 --> 00:44:08,640
equal causation in other words
1310
00:44:08,640 --> 00:44:10,800
just because two pieces of data are both
1311
00:44:10,800 --> 00:44:12,560
trending in the same direction
1312
00:44:12,560 --> 00:44:14,640
that doesn't necessarily mean they are
1313
00:44:14,640 --> 00:44:16,079
all related
1314
00:44:16,079 --> 00:44:17,920
we'll learn more about that later and
1315
00:44:17,920 --> 00:44:19,920
now the final piece
1316
00:44:19,920 --> 00:44:22,319
of the analytical thinking puzzle big
1317
00:44:22,319 --> 00:44:23,839
picture thinking
1318
00:44:23,839 --> 00:44:25,440
this means being able to see the big
1319
00:44:25,440 --> 00:44:27,599
picture as well as the details
1320
00:44:27,599 --> 00:44:29,359
a jigsaw puzzle is a great way to think
1321
00:44:29,359 --> 00:44:31,520
about this big picture thinking
1322
00:44:31,520 --> 00:44:33,839
is like looking at a complete puzzle you
1323
00:44:33,839 --> 00:44:35,440
can enjoy the whole picture
1324
00:44:35,440 --> 00:44:36,960
without getting stuck on every tiny
1325
00:44:36,960 --> 00:44:39,440
piece that went into making it
1326
00:44:39,440 --> 00:44:42,319
if you only focus on individual pieces
1327
00:44:42,319 --> 00:44:43,760
you wouldn't be able to see
1328
00:44:43,760 --> 00:44:46,960
past that which is why big picture
1329
00:44:46,960 --> 00:44:47,440
thinking
1330
00:44:47,440 --> 00:44:50,480
is so important it helps you zoom out
1331
00:44:50,480 --> 00:44:53,200
and see possibilities and opportunities
1332
00:44:53,200 --> 00:44:54,720
this leads to exciting
1333
00:44:54,720 --> 00:44:57,280
new ideas or innovations on the flip
1334
00:44:57,280 --> 00:44:57,920
side
1335
00:44:57,920 --> 00:45:00,160
detail-oriented thinking is all about
1336
00:45:00,160 --> 00:45:01,040
figuring out
1337
00:45:01,040 --> 00:45:02,720
all of the aspects that will help you
1338
00:45:02,720 --> 00:45:04,240
execute a plan
1339
00:45:04,240 --> 00:45:06,480
in other words the pieces that make up
1340
00:45:06,480 --> 00:45:07,760
your puzzle
1341
00:45:07,760 --> 00:45:09,359
there are all kinds of problems in the
1342
00:45:09,359 --> 00:45:11,119
business world that can benefit
1343
00:45:11,119 --> 00:45:13,280
from employees who have both a big
1344
00:45:13,280 --> 00:45:15,200
picture and a detail-oriented way of
1345
00:45:15,200 --> 00:45:15,680
thinking
1346
00:45:15,680 --> 00:45:17,440
most of us are naturally better at one
1347
00:45:17,440 --> 00:45:19,680
or the other but you can always develop
1348
00:45:19,680 --> 00:45:20,720
the skills to fit
1349
00:45:20,720 --> 00:45:23,119
both pieces together and now that you
1350
00:45:23,119 --> 00:45:24,880
know the five aspects of analytical
1351
00:45:24,880 --> 00:45:25,760
thinking
1352
00:45:25,760 --> 00:45:28,240
visualization strategy problem
1353
00:45:28,240 --> 00:45:29,440
orientation
1354
00:45:29,440 --> 00:45:31,599
correlation and big picture and
1355
00:45:31,599 --> 00:45:33,280
detail-oriented thinking
1356
00:45:33,280 --> 00:45:35,119
you can put them to work for you when
1357
00:45:35,119 --> 00:45:36,560
you're working with data
1358
00:45:36,560 --> 00:45:38,560
and as you continue through this course
1359
00:45:38,560 --> 00:45:41,750
you'll learn how
1360
00:45:41,760 --> 00:45:43,359
let's recap what we've learned about
1361
00:45:43,359 --> 00:45:45,440
analytical thinking so far
1362
00:45:45,440 --> 00:45:48,319
the five key aspects are visualization
1363
00:45:48,319 --> 00:45:49,280
strategy
1364
00:45:49,280 --> 00:45:52,160
problem orientation correlation and
1365
00:45:52,160 --> 00:45:53,200
using big picture
1366
00:45:53,200 --> 00:45:56,160
and detail-oriented thinking and we've
1367
00:45:56,160 --> 00:45:57,920
seen how you already use them
1368
00:45:57,920 --> 00:46:00,480
in your everyday life we also talked
1369
00:46:00,480 --> 00:46:01,920
about how different people
1370
00:46:01,920 --> 00:46:04,240
naturally use certain types of thinking
1371
00:46:04,240 --> 00:46:06,160
but that you can absolutely grow
1372
00:46:06,160 --> 00:46:08,160
and develop the skills that might not
1373
00:46:08,160 --> 00:46:10,240
come as easily to you
1374
00:46:10,240 --> 00:46:12,240
this means you can become a versatile
1375
00:46:12,240 --> 00:46:14,079
thinker which is a very important
1376
00:46:14,079 --> 00:46:16,079
part of data analysis you might
1377
00:46:16,079 --> 00:46:18,079
naturally be an analytical thinker
1378
00:46:18,079 --> 00:46:20,079
but you can learn to think creatively
1379
00:46:20,079 --> 00:46:21,280
and critically
1380
00:46:21,280 --> 00:46:24,240
and be great at all three the more ways
1381
00:46:24,240 --> 00:46:25,119
you can think
1382
00:46:25,119 --> 00:46:26,880
the easier it is to think outside the
1383
00:46:26,880 --> 00:46:29,680
box and come up with fresh ideas
1384
00:46:29,680 --> 00:46:31,359
but why is it important to think in
1385
00:46:31,359 --> 00:46:33,520
different ways well because in data
1386
00:46:33,520 --> 00:46:34,400
analysis
1387
00:46:34,400 --> 00:46:36,160
solutions are almost never right in
1388
00:46:36,160 --> 00:46:37,760
front of you you need to think
1389
00:46:37,760 --> 00:46:39,200
critically to find out
1390
00:46:39,200 --> 00:46:41,599
the right questions to ask but you also
1391
00:46:41,599 --> 00:46:43,119
need to think creatively
1392
00:46:43,119 --> 00:46:46,160
to get new and unexpected answers let's
1393
00:46:46,160 --> 00:46:47,760
talk about some of the questions
1394
00:46:47,760 --> 00:46:49,839
data analysts ask when they're on the
1395
00:46:49,839 --> 00:46:51,760
hunt for a solution
1396
00:46:51,760 --> 00:46:54,800
here's one that will come up a lot what
1397
00:46:54,800 --> 00:46:57,200
is the root cause of a problem
1398
00:46:57,200 --> 00:46:59,200
a root cause is the reason why a problem
1399
00:46:59,200 --> 00:47:00,240
occurs
1400
00:47:00,240 --> 00:47:02,400
if we can identify and get rid of a root
1401
00:47:02,400 --> 00:47:03,359
cross
1402
00:47:03,359 --> 00:47:04,960
we can prevent that problem from
1403
00:47:04,960 --> 00:47:06,480
happening again
1404
00:47:06,480 --> 00:47:08,160
a simple way to wrap your head around
1405
00:47:08,160 --> 00:47:10,800
root causes is with the process called
1406
00:47:10,800 --> 00:47:14,480
the five wise in the five whys you ask
1407
00:47:14,480 --> 00:47:18,640
why five times to reveal the root cause
1408
00:47:18,640 --> 00:47:20,960
the fifth and final answer should give
1409
00:47:20,960 --> 00:47:22,079
you some useful
1410
00:47:22,079 --> 00:47:25,040
and sometimes surprising insights here's
1411
00:47:25,040 --> 00:47:28,079
an example of the five why's in action
1412
00:47:28,079 --> 00:47:30,000
let's say you wanted to make a blueberry
1413
00:47:30,000 --> 00:47:32,720
pie but couldn't find any blueberries
1414
00:47:32,720 --> 00:47:34,400
you'd be trying to solve a problem by
1415
00:47:34,400 --> 00:47:37,680
asking why can't i make a blueberry pie
1416
00:47:37,680 --> 00:47:39,599
the answer would be there are no
1417
00:47:39,599 --> 00:47:41,920
blueberries at the store
1418
00:47:41,920 --> 00:47:45,839
there's why number one so you then ask
1419
00:47:45,839 --> 00:47:47,200
why were there no blueberries at the
1420
00:47:47,200 --> 00:47:49,200
store and discover
1421
00:47:49,200 --> 00:47:50,720
that the blueberry bushes don't have
1422
00:47:50,720 --> 00:47:52,319
enough fruit this season
1423
00:47:52,319 --> 00:47:56,400
that's why number two next you'd ask
1424
00:47:56,400 --> 00:47:58,559
why was there not enough fruit this
1425
00:47:58,559 --> 00:48:00,559
would lead to the fact that birds were
1426
00:48:00,559 --> 00:48:02,079
eating all the berries
1427
00:48:02,079 --> 00:48:05,599
why number three asked and answered
1428
00:48:05,599 --> 00:48:08,880
now we get to why number four ask
1429
00:48:08,880 --> 00:48:11,040
why a fourth time and the answer would
1430
00:48:11,040 --> 00:48:12,800
be that although the birds
1431
00:48:12,800 --> 00:48:15,359
normally prefer mulberries and don't eat
1432
00:48:15,359 --> 00:48:16,319
blueberries
1433
00:48:16,319 --> 00:48:18,319
the mulberry bushes didn't produce fruit
1434
00:48:18,319 --> 00:48:19,359
this season
1435
00:48:19,359 --> 00:48:20,960
so the birds are eating blueberries
1436
00:48:20,960 --> 00:48:23,040
instead and finally
1437
00:48:23,040 --> 00:48:25,200
we get to why number five which should
1438
00:48:25,200 --> 00:48:26,960
reveal the root cause
1439
00:48:26,960 --> 00:48:29,440
a late frost damaged the mulberry bushes
1440
00:48:29,440 --> 00:48:31,760
so they didn't produce any fruit
1441
00:48:31,760 --> 00:48:34,079
so you can't make a blueberry pie
1442
00:48:34,079 --> 00:48:35,599
because of a late frost
1443
00:48:35,599 --> 00:48:38,240
months ago see how the five wives can
1444
00:48:38,240 --> 00:48:38,800
reveal
1445
00:48:38,800 --> 00:48:41,520
some very surprising root causes this is
1446
00:48:41,520 --> 00:48:43,040
a great trick to know
1447
00:48:43,040 --> 00:48:45,680
and it can be a very helpful process in
1448
00:48:45,680 --> 00:48:47,280
data analysis
1449
00:48:47,280 --> 00:48:49,680
another question commonly asked by data
1450
00:48:49,680 --> 00:48:50,319
analysts
1451
00:48:50,319 --> 00:48:53,520
is where are the gaps in our process
1452
00:48:53,520 --> 00:48:56,000
for this many people will use something
1453
00:48:56,000 --> 00:48:57,680
called gap analysis
1454
00:48:57,680 --> 00:48:59,520
gap analysis lets you examine and
1455
00:48:59,520 --> 00:49:02,000
evaluate how a process works currently
1456
00:49:02,000 --> 00:49:03,599
in order to get where you want to be in
1457
00:49:03,599 --> 00:49:05,040
the future
1458
00:49:05,040 --> 00:49:07,359
businesses conduct gap analysis to do
1459
00:49:07,359 --> 00:49:08,720
all kinds of things
1460
00:49:08,720 --> 00:49:10,720
such as improve a product or become more
1461
00:49:10,720 --> 00:49:12,559
efficient the general approach to gap
1462
00:49:12,559 --> 00:49:13,359
analysis
1463
00:49:13,359 --> 00:49:15,280
is understanding where you are now
1464
00:49:15,280 --> 00:49:17,920
compared to where you want to be
1465
00:49:17,920 --> 00:49:19,920
then you can identify the gaps that
1466
00:49:19,920 --> 00:49:22,240
exist between a current and future state
1467
00:49:22,240 --> 00:49:24,960
and determine how to bridge them a third
1468
00:49:24,960 --> 00:49:27,280
question that data analysts ask a lot is
1469
00:49:27,280 --> 00:49:29,760
what did we not consider before this is
1470
00:49:29,760 --> 00:49:31,359
a great way to think about
1471
00:49:31,359 --> 00:49:34,160
what information or procedure might be
1472
00:49:34,160 --> 00:49:35,520
missing from a process
1473
00:49:35,520 --> 00:49:37,760
so you can identify ways to make better
1474
00:49:37,760 --> 00:49:39,119
decisions and strategies
1475
00:49:39,119 --> 00:49:41,440
moving forward these are just a few
1476
00:49:41,440 --> 00:49:43,440
examples of the kinds of questions data
1477
00:49:43,440 --> 00:49:44,400
analysts use
1478
00:49:44,400 --> 00:49:46,640
at their jobs every day as you begin
1479
00:49:46,640 --> 00:49:47,760
your career
1480
00:49:47,760 --> 00:49:49,359
i'm sure you'll think of a whole lot
1481
00:49:49,359 --> 00:49:52,319
more the way data analysts think and ask
1482
00:49:52,319 --> 00:49:54,480
questions plays a big part in how
1483
00:49:54,480 --> 00:49:56,160
businesses make decisions
1484
00:49:56,160 --> 00:49:58,240
that's why analytical thinking and
1485
00:49:58,240 --> 00:49:59,760
understanding how to ask the right
1486
00:49:59,760 --> 00:50:00,400
questions
1487
00:50:00,400 --> 00:50:02,160
can have such a huge impact on the
1488
00:50:02,160 --> 00:50:04,720
overall success of a business
1489
00:50:04,720 --> 00:50:06,720
later we'll talk more about how
1490
00:50:06,720 --> 00:50:08,240
data-driven decisions
1491
00:50:08,240 --> 00:50:11,760
can lead to successful outcomes
1492
00:50:11,760 --> 00:50:13,839
in an earlier video you learned about
1493
00:50:13,839 --> 00:50:16,480
five essential analytical skills
1494
00:50:16,480 --> 00:50:19,200
as a reminder their curiosity
1495
00:50:19,200 --> 00:50:20,800
understanding context
1496
00:50:20,800 --> 00:50:23,680
having a technical mindset data design
1497
00:50:23,680 --> 00:50:26,720
and data strategy in the next couple of
1498
00:50:26,720 --> 00:50:27,440
videos
1499
00:50:27,440 --> 00:50:29,119
we'll explore how these abilities all
1500
00:50:29,119 --> 00:50:31,119
become part of data-driven decision
1501
00:50:31,119 --> 00:50:31,760
making
1502
00:50:31,760 --> 00:50:33,760
but first let's look at the concept of
1503
00:50:33,760 --> 00:50:35,680
data-driven decision making
1504
00:50:35,680 --> 00:50:37,839
and why it's more likely to lead to
1505
00:50:37,839 --> 00:50:39,599
successful outcomes
1506
00:50:39,599 --> 00:50:41,119
you might remember that data driven
1507
00:50:41,119 --> 00:50:43,520
decision making involves using facts
1508
00:50:43,520 --> 00:50:46,000
to guide business strategy data analysts
1509
00:50:46,000 --> 00:50:47,680
can tap into the power of data
1510
00:50:47,680 --> 00:50:50,240
to do all kinds of amazing things with
1511
00:50:50,240 --> 00:50:50,960
data
1512
00:50:50,960 --> 00:50:53,359
they can gain valuable insights verify
1513
00:50:53,359 --> 00:50:55,280
their theories or assumptions
1514
00:50:55,280 --> 00:50:57,040
better understand opportunities and
1515
00:50:57,040 --> 00:50:59,920
challenges support an objective
1516
00:50:59,920 --> 00:51:03,440
help make a plan and much more
1517
00:51:03,440 --> 00:51:05,760
in business data-driven decision making
1518
00:51:05,760 --> 00:51:07,599
can improve the results in a lot of
1519
00:51:07,599 --> 00:51:08,800
different ways
1520
00:51:08,800 --> 00:51:11,200
for example say a dairy farmer wants to
1521
00:51:11,200 --> 00:51:11,920
start making
1522
00:51:11,920 --> 00:51:14,079
and selling ice cream they could guess
1523
00:51:14,079 --> 00:51:15,839
what flavors customers would like
1524
00:51:15,839 --> 00:51:17,280
but there's a better way to get the
1525
00:51:17,280 --> 00:51:19,920
information the farmer could survey
1526
00:51:19,920 --> 00:51:20,720
people
1527
00:51:20,720 --> 00:51:23,599
and ask them what flavors they prefer
1528
00:51:23,599 --> 00:51:25,839
this gives the farmer the data they need
1529
00:51:25,839 --> 00:51:27,839
to pick ice cream flavors people will
1530
00:51:27,839 --> 00:51:29,040
enjoy
1531
00:51:29,040 --> 00:51:31,119
here's another example let's say the
1532
00:51:31,119 --> 00:51:32,480
president of an organization
1533
00:51:32,480 --> 00:51:34,640
is curious about what perks employees
1534
00:51:34,640 --> 00:51:36,000
value most
1535
00:51:36,000 --> 00:51:38,319
she asked the human resources director
1536
00:51:38,319 --> 00:51:39,599
who says people value
1537
00:51:39,599 --> 00:51:42,559
casual dress code it's a gut feeling but
1538
00:51:42,559 --> 00:51:44,319
the hr director backs it up with the
1539
00:51:44,319 --> 00:51:45,200
fact that
1540
00:51:45,200 --> 00:51:47,680
he sees a lot of people wearing jeans
1541
00:51:47,680 --> 00:51:49,119
and t-shirts
1542
00:51:49,119 --> 00:51:51,040
but what if this company were to use a
1543
00:51:51,040 --> 00:51:52,640
more structured employee feedback
1544
00:51:52,640 --> 00:51:53,680
process
1545
00:51:53,680 --> 00:51:56,319
such as a survey it might reveal that
1546
00:51:56,319 --> 00:51:56,960
employees
1547
00:51:56,960 --> 00:51:58,400
actually enjoy free public
1548
00:51:58,400 --> 00:52:00,880
transportation cards the most
1549
00:52:00,880 --> 00:52:03,119
the human resources director just didn't
1550
00:52:03,119 --> 00:52:04,480
realize that because
1551
00:52:04,480 --> 00:52:07,200
he drives to work these are just some of
1552
00:52:07,200 --> 00:52:08,000
the benefits
1553
00:52:08,000 --> 00:52:10,559
of data-driven decision making it gives
1554
00:52:10,559 --> 00:52:12,720
you greater confidence about your choice
1555
00:52:12,720 --> 00:52:14,400
and your abilities to address business
1556
00:52:14,400 --> 00:52:16,400
challenges it helps you become more
1557
00:52:16,400 --> 00:52:17,359
proactive
1558
00:52:17,359 --> 00:52:19,520
when an opportunity presents itself and
1559
00:52:19,520 --> 00:52:21,440
it saves you time and effort when
1560
00:52:21,440 --> 00:52:23,280
working towards a goal
1561
00:52:23,280 --> 00:52:24,800
now let's learn more about how these
1562
00:52:24,800 --> 00:52:26,880
five skills help you tap into all the
1563
00:52:26,880 --> 00:52:29,599
potential of data-driven decision-making
1564
00:52:29,599 --> 00:52:33,280
first think about curiosity and context
1565
00:52:33,280 --> 00:52:34,960
the more you learn about the power of
1566
00:52:34,960 --> 00:52:37,119
data the more curious you're likely to
1567
00:52:37,119 --> 00:52:38,000
become
1568
00:52:38,000 --> 00:52:39,359
you'll start to see patterns in
1569
00:52:39,359 --> 00:52:41,280
relationships in everyday life
1570
00:52:41,280 --> 00:52:43,280
whether you're reading the news watching
1571
00:52:43,280 --> 00:52:45,200
a movie or going to an appointment
1572
00:52:45,200 --> 00:52:46,800
across town
1573
00:52:46,800 --> 00:52:49,040
the analysts take their thinking in a
1574
00:52:49,040 --> 00:52:49,920
step further
1575
00:52:49,920 --> 00:52:52,400
by using context to make predictions
1576
00:52:52,400 --> 00:52:52,960
research
1577
00:52:52,960 --> 00:52:56,000
answers and eventually draw conclusions
1578
00:52:56,000 --> 00:52:57,760
about what they've discovered
1579
00:52:57,760 --> 00:52:59,920
this natural process is a great first
1580
00:52:59,920 --> 00:53:02,960
step in becoming more data driven
1581
00:53:02,960 --> 00:53:05,599
having a technical mindset comes next
1582
00:53:05,599 --> 00:53:06,319
everyone has
1583
00:53:06,319 --> 00:53:09,040
instincts or as in the case of our human
1584
00:53:09,040 --> 00:53:10,960
resources director example
1585
00:53:10,960 --> 00:53:13,760
gut feelings data analysts are no
1586
00:53:13,760 --> 00:53:14,640
different
1587
00:53:14,640 --> 00:53:16,960
they have gut feelings too but they've
1588
00:53:16,960 --> 00:53:18,079
trained themselves
1589
00:53:18,079 --> 00:53:20,400
to build on those feelings and use a
1590
00:53:20,400 --> 00:53:23,119
more technical approach to explore them
1591
00:53:23,119 --> 00:53:25,040
they do this by always seeking out the
1592
00:53:25,040 --> 00:53:27,040
facts putting them to work
1593
00:53:27,040 --> 00:53:29,520
through analysis and using the insights
1594
00:53:29,520 --> 00:53:32,559
they gain to make informed decisions
1595
00:53:32,559 --> 00:53:35,440
next we come to data design which has a
1596
00:53:35,440 --> 00:53:37,280
strong connection to data driven
1597
00:53:37,280 --> 00:53:38,400
decision making
1598
00:53:38,400 --> 00:53:41,040
to put it simply designing your data so
1599
00:53:41,040 --> 00:53:42,880
that is organized in a logical way
1600
00:53:42,880 --> 00:53:44,640
makes it easy for data analysts to
1601
00:53:44,640 --> 00:53:46,480
access understand
1602
00:53:46,480 --> 00:53:48,240
and make the most of available
1603
00:53:48,240 --> 00:53:49,839
information
1604
00:53:49,839 --> 00:53:52,000
and it's important to keep in mind that
1605
00:53:52,000 --> 00:53:53,119
data design
1606
00:53:53,119 --> 00:53:55,520
doesn't just apply to databases this
1607
00:53:55,520 --> 00:53:56,720
kind of thinking
1608
00:53:56,720 --> 00:53:58,640
can work with all sorts of real life
1609
00:53:58,640 --> 00:54:00,480
situations too
1610
00:54:00,480 --> 00:54:03,760
the basic idea is this if you make
1611
00:54:03,760 --> 00:54:06,000
decisions that are informed by data
1612
00:54:06,000 --> 00:54:07,839
you are more likely to make more
1613
00:54:07,839 --> 00:54:10,720
informed and effective decisions
1614
00:54:10,720 --> 00:54:13,119
the final ability is data strategy which
1615
00:54:13,119 --> 00:54:14,480
incorporates the people
1616
00:54:14,480 --> 00:54:16,559
processes and tools used to solve a
1617
00:54:16,559 --> 00:54:18,720
problem this is a big one to remember
1618
00:54:18,720 --> 00:54:19,280
because
1619
00:54:19,280 --> 00:54:21,520
data strategy gives you a high level
1620
00:54:21,520 --> 00:54:22,800
view of the path
1621
00:54:22,800 --> 00:54:24,400
you'll need to take to achieve your
1622
00:54:24,400 --> 00:54:26,160
goals also
1623
00:54:26,160 --> 00:54:28,400
data-driven decision making isn't a
1624
00:54:28,400 --> 00:54:30,079
one-person job
1625
00:54:30,079 --> 00:54:32,000
it's much more likely to be successful
1626
00:54:32,000 --> 00:54:33,520
if everyone is on board
1627
00:54:33,520 --> 00:54:35,839
and on the same page so it's important
1628
00:54:35,839 --> 00:54:36,559
to make sure
1629
00:54:36,559 --> 00:54:39,200
specific procedures are in place and
1630
00:54:39,200 --> 00:54:41,119
that your technology being used
1631
00:54:41,119 --> 00:54:42,559
is aligned with your data-driven
1632
00:54:42,559 --> 00:54:44,400
strategy
1633
00:54:44,400 --> 00:54:46,160
now you know how these five essential
1634
00:54:46,160 --> 00:54:48,319
analytical skills work towards making
1635
00:54:48,319 --> 00:54:48,880
better
1636
00:54:48,880 --> 00:54:52,400
data-driven decisions so far many of the
1637
00:54:52,400 --> 00:54:54,640
examples you've heard are hypothetical
1638
00:54:54,640 --> 00:54:56,559
that means they could be true in theory
1639
00:54:56,559 --> 00:54:57,680
but aren't specific
1640
00:54:57,680 --> 00:55:01,119
real world cases next we'll look at some
1641
00:55:01,119 --> 00:55:02,400
real examples
1642
00:55:02,400 --> 00:55:04,400
i can't wait to share how data analysts
1643
00:55:04,400 --> 00:55:05,599
put data to work
1644
00:55:05,599 --> 00:55:08,880
for amazing results
1645
00:55:08,880 --> 00:55:11,040
in this video i'm going to share some
1646
00:55:11,040 --> 00:55:11,920
case studies
1647
00:55:11,920 --> 00:55:13,680
that highlight the incredible work data
1648
00:55:13,680 --> 00:55:15,200
analysts do
1649
00:55:15,200 --> 00:55:17,040
each of these scenarios shows off the
1650
00:55:17,040 --> 00:55:19,119
power of data driven decision making
1651
00:55:19,119 --> 00:55:21,680
in unexpected ways the first story is
1652
00:55:21,680 --> 00:55:23,040
about google
1653
00:55:23,040 --> 00:55:25,280
as i mentioned a little while back here
1654
00:55:25,280 --> 00:55:26,160
at google
1655
00:55:26,160 --> 00:55:28,000
our mission is to organize the world's
1656
00:55:28,000 --> 00:55:29,920
information and make it universally
1657
00:55:29,920 --> 00:55:30,720
accessible
1658
00:55:30,720 --> 00:55:33,680
and useful all of our products from idea
1659
00:55:33,680 --> 00:55:34,640
to development
1660
00:55:34,640 --> 00:55:37,119
to launch are built on data and data
1661
00:55:37,119 --> 00:55:39,599
driven decision making
1662
00:55:39,599 --> 00:55:41,119
there are tons of examples here at
1663
00:55:41,119 --> 00:55:43,200
google of people using facts to create
1664
00:55:43,200 --> 00:55:44,799
business strategy
1665
00:55:44,799 --> 00:55:47,280
but one of the most famous ones has to
1666
00:55:47,280 --> 00:55:49,440
do with google's human resources
1667
00:55:49,440 --> 00:55:52,000
so here's how it went the hr department
1668
00:55:52,000 --> 00:55:52,799
wanted to know
1669
00:55:52,799 --> 00:55:55,280
if there was value in having managers
1670
00:55:55,280 --> 00:55:57,440
were their contributions worthwhile
1671
00:55:57,440 --> 00:55:58,960
or should everyone just be an individual
1672
00:55:58,960 --> 00:56:00,640
contributor
1673
00:56:00,640 --> 00:56:02,799
to answer that question google's people
1674
00:56:02,799 --> 00:56:04,079
analytics team
1675
00:56:04,079 --> 00:56:06,000
look at past performance reviews and
1676
00:56:06,000 --> 00:56:07,440
employee surveys
1677
00:56:07,440 --> 00:56:09,440
the data they found was plotted on a
1678
00:56:09,440 --> 00:56:10,640
graph because
1679
00:56:10,640 --> 00:56:13,040
as you've learned visuals are extremely
1680
00:56:13,040 --> 00:56:14,960
helpful when trying to understand a
1681
00:56:14,960 --> 00:56:16,880
problem or concept
1682
00:56:16,880 --> 00:56:18,960
the graph revealed that googlers had
1683
00:56:18,960 --> 00:56:21,200
positive feelings about their managers
1684
00:56:21,200 --> 00:56:23,119
but the data was pretty general and the
1685
00:56:23,119 --> 00:56:25,119
team wanted to learn more
1686
00:56:25,119 --> 00:56:27,280
so they dug deeper and split the data
1687
00:56:27,280 --> 00:56:28,559
into quartiles
1688
00:56:28,559 --> 00:56:30,799
a quartile divides data points into four
1689
00:56:30,799 --> 00:56:31,680
equal parts
1690
00:56:31,680 --> 00:56:34,160
or quarters here's where the really cool
1691
00:56:34,160 --> 00:56:35,760
stuff started happening
1692
00:56:35,760 --> 00:56:37,680
the data analyst discovered that there
1693
00:56:37,680 --> 00:56:39,280
was a big difference between the very
1694
00:56:39,280 --> 00:56:39,920
top
1695
00:56:39,920 --> 00:56:42,640
and the very bottom quartiles as it
1696
00:56:42,640 --> 00:56:43,520
turned out
1697
00:56:43,520 --> 00:56:45,359
the teams with the best managers were
1698
00:56:45,359 --> 00:56:47,040
significantly happier
1699
00:56:47,040 --> 00:56:49,760
more productive and more likely to want
1700
00:56:49,760 --> 00:56:52,480
to keep working at google
1701
00:56:52,480 --> 00:56:55,520
this confirmed that managers were valued
1702
00:56:55,520 --> 00:56:58,319
and make a big difference therefore the
1703
00:56:58,319 --> 00:57:00,000
idea of having only individual
1704
00:57:00,000 --> 00:57:00,960
contributors
1705
00:57:00,960 --> 00:57:03,599
was not implemented but there was still
1706
00:57:03,599 --> 00:57:04,799
more work to do
1707
00:57:04,799 --> 00:57:06,640
just knowing that great managers create
1708
00:57:06,640 --> 00:57:08,880
great results doesn't lead to actionable
1709
00:57:08,880 --> 00:57:10,000
insights
1710
00:57:10,000 --> 00:57:12,079
you have to identify what exactly makes
1711
00:57:12,079 --> 00:57:13,680
a great manager
1712
00:57:13,680 --> 00:57:16,079
so the team took two additional steps to
1713
00:57:16,079 --> 00:57:18,000
collect more data
1714
00:57:18,000 --> 00:57:20,559
first they launched an awards program
1715
00:57:20,559 --> 00:57:22,160
where employees could nominate their
1716
00:57:22,160 --> 00:57:24,000
favorite managers
1717
00:57:24,000 --> 00:57:26,000
for every submission you had to provide
1718
00:57:26,000 --> 00:57:27,920
examples or data
1719
00:57:27,920 --> 00:57:30,799
about what makes that manager great the
1720
00:57:30,799 --> 00:57:32,480
second step involved
1721
00:57:32,480 --> 00:57:34,640
interviewing managers who are graft on
1722
00:57:34,640 --> 00:57:35,680
the top
1723
00:57:35,680 --> 00:57:38,160
and bottom quartiles this helped the
1724
00:57:38,160 --> 00:57:39,119
analytics team
1725
00:57:39,119 --> 00:57:41,680
see the differences between successful
1726
00:57:41,680 --> 00:57:44,640
and less successful management behaviors
1727
00:57:44,640 --> 00:57:47,119
the best behaviors were identified as
1728
00:57:47,119 --> 00:57:48,960
were the most common reasons for a
1729
00:57:48,960 --> 00:57:51,040
manager needing improvement
1730
00:57:51,040 --> 00:57:52,559
the final step was sharing these
1731
00:57:52,559 --> 00:57:54,400
insights and putting a procedure in
1732
00:57:54,400 --> 00:57:54,960
place
1733
00:57:54,960 --> 00:57:56,640
for evaluating managers with these
1734
00:57:56,640 --> 00:57:58,319
qualities in mind
1735
00:57:58,319 --> 00:58:00,400
this data-driven decision continues to
1736
00:58:00,400 --> 00:58:02,400
create an exceptional company culture
1737
00:58:02,400 --> 00:58:06,960
for my colleagues and me thanks data
1738
00:58:06,960 --> 00:58:08,880
another interesting example comes from
1739
00:58:08,880 --> 00:58:11,040
the nonprofit sector
1740
00:58:11,040 --> 00:58:13,359
nonprofits are organizations dedicated
1741
00:58:13,359 --> 00:58:14,480
to advancing
1742
00:58:14,480 --> 00:58:17,119
a social cause or advocating for a
1743
00:58:17,119 --> 00:58:18,319
particular effort
1744
00:58:18,319 --> 00:58:21,520
such as food security education or the
1745
00:58:21,520 --> 00:58:22,640
arts
1746
00:58:22,640 --> 00:58:24,880
in this case data analysts research how
1747
00:58:24,880 --> 00:58:26,960
journalists can make a more meaningful
1748
00:58:26,960 --> 00:58:28,720
impact for the non-profits they would
1749
00:58:28,720 --> 00:58:30,880
write about because journalists write
1750
00:58:30,880 --> 00:58:32,000
for newspapers
1751
00:58:32,000 --> 00:58:34,400
magazines and other news outlets they
1752
00:58:34,400 --> 00:58:36,400
can help non-profits reach readers like
1753
00:58:36,400 --> 00:58:37,440
you and me
1754
00:58:37,440 --> 00:58:40,240
who then take action to help non-profits
1755
00:58:40,240 --> 00:58:41,760
reach their goals
1756
00:58:41,760 --> 00:58:43,920
for instance say you read about the
1757
00:58:43,920 --> 00:58:45,440
problem of climate change
1758
00:58:45,440 --> 00:58:48,160
in an online magazine if the article is
1759
00:58:48,160 --> 00:58:49,040
effective
1760
00:58:49,040 --> 00:58:51,040
you learn more about the cause and might
1761
00:58:51,040 --> 00:58:52,640
even be compelled to make greener
1762
00:58:52,640 --> 00:58:54,720
choices in your day-to-day life
1763
00:58:54,720 --> 00:58:57,119
volunteer for a non-profit or make a
1764
00:58:57,119 --> 00:58:58,480
donation
1765
00:58:58,480 --> 00:59:00,799
that's an example of the journalists
1766
00:59:00,799 --> 00:59:03,119
work bringing about awareness
1767
00:59:03,119 --> 00:59:06,720
understanding and engagement so
1768
00:59:06,720 --> 00:59:09,520
back to the story the data analysts used
1769
00:59:09,520 --> 00:59:11,920
the tracker to monitor story topics
1770
00:59:11,920 --> 00:59:15,119
clicks web traffic comments shares
1771
00:59:15,119 --> 00:59:18,000
and more then they evaluated the
1772
00:59:18,000 --> 00:59:18,880
information
1773
00:59:18,880 --> 00:59:20,400
to make recommendations for how the
1774
00:59:20,400 --> 00:59:21,760
journalists could do their jobs even
1775
00:59:21,760 --> 00:59:22,880
better
1776
00:59:22,880 --> 00:59:25,040
in the end they came up with some great
1777
00:59:25,040 --> 00:59:27,040
ideas for how nonprofits
1778
00:59:27,040 --> 00:59:29,280
and journalists can motivate people
1779
00:59:29,280 --> 00:59:30,480
everywhere to work together
1780
00:59:30,480 --> 00:59:32,319
and make the world a better place
1781
00:59:32,319 --> 00:59:34,000
there's really no end to what you can do
1782
00:59:34,000 --> 00:59:35,520
as a data analyst
1783
00:59:35,520 --> 00:59:37,280
as you progress through this program
1784
00:59:37,280 --> 00:59:40,240
you'll discover even more possibilities
1785
00:59:40,240 --> 00:59:41,599
great job following along with the
1786
00:59:41,599 --> 00:59:43,520
topics in these past few videos
1787
00:59:43,520 --> 00:59:45,680
you learned all about analytical skills
1788
00:59:45,680 --> 00:59:47,839
and the five key characteristics of data
1789
00:59:47,839 --> 00:59:48,559
analysts
1790
00:59:48,559 --> 00:59:50,960
you probably even learned that you're a
1791
00:59:50,960 --> 00:59:53,680
pro at most of these already
1792
00:59:53,680 --> 00:59:55,839
next you discovered what it means to
1793
00:59:55,839 --> 00:59:57,359
think analytically
1794
00:59:57,359 --> 00:59:59,839
and the specific skills data analysts
1795
00:59:59,839 --> 01:00:00,640
develop
1796
01:00:00,640 --> 01:00:03,280
to help them do it you explore tools and
1797
01:00:03,280 --> 01:00:04,160
processes
1798
01:00:04,160 --> 01:00:06,480
that enable data analysts to pinpoint a
1799
01:00:06,480 --> 01:00:08,480
problem and ask the right questions in
1800
01:00:08,480 --> 01:00:10,079
order to solve it
1801
01:00:10,079 --> 01:00:12,480
finally some real world stories helped
1802
01:00:12,480 --> 01:00:13,760
illustrate why data-driven
1803
01:00:13,760 --> 01:00:14,720
decision-making
1804
01:00:14,720 --> 01:00:16,480
is usually more successful than other
1805
01:00:16,480 --> 01:00:18,480
methods you're building a wonderful
1806
01:00:18,480 --> 01:00:20,160
foundation for your career as a data
1807
01:00:20,160 --> 01:00:20,880
analyst
1808
01:00:20,880 --> 01:00:23,200
with every video your skills will
1809
01:00:23,200 --> 01:00:24,559
continue to expand
1810
01:00:24,559 --> 01:00:26,240
and your understanding of key data
1811
01:00:26,240 --> 01:00:28,240
analytics concepts will only get
1812
01:00:28,240 --> 01:00:29,440
stronger
1813
01:00:29,440 --> 01:00:31,599
soon you'll have a chance to test out
1814
01:00:31,599 --> 01:00:33,119
everything you've learned
1815
01:00:33,119 --> 01:00:34,799
this is a really useful opportunity to
1816
01:00:34,799 --> 01:00:36,240
check your understanding of
1817
01:00:36,240 --> 01:00:38,640
all the concepts we've discussed and if
1818
01:00:38,640 --> 01:00:40,559
you're ever unsure about a question
1819
01:00:40,559 --> 01:00:42,480
you can review the videos and readings
1820
01:00:42,480 --> 01:00:44,160
to find the answer
1821
01:00:44,160 --> 01:00:46,319
this is another awesome way to practice
1822
01:00:46,319 --> 01:00:47,680
collecting data
1823
01:00:47,680 --> 01:00:50,870
keep up the great work
1824
01:00:50,880 --> 01:00:53,680
hey it's great to have you back so we've
1825
01:00:53,680 --> 01:00:55,280
talked a little bit about the data
1826
01:00:55,280 --> 01:00:58,400
analysis process as a quick refresher
1827
01:00:58,400 --> 01:01:01,760
the data analysis process phases are ask
1828
01:01:01,760 --> 01:01:04,880
prepare process analyze
1829
01:01:04,880 --> 01:01:07,680
share and act you might remember me
1830
01:01:07,680 --> 01:01:08,720
saying earlier
1831
01:01:08,720 --> 01:01:10,799
that this entire program is modeled
1832
01:01:10,799 --> 01:01:12,400
after these steps
1833
01:01:12,400 --> 01:01:15,200
so now we're going to really dig in and
1834
01:01:15,200 --> 01:01:17,040
explore how each of these phases work
1835
01:01:17,040 --> 01:01:17,839
together
1836
01:01:17,839 --> 01:01:20,000
but i'm getting a little ahead of myself
1837
01:01:20,000 --> 01:01:21,839
first let's spend a little time
1838
01:01:21,839 --> 01:01:25,040
understanding the data life cycle no
1839
01:01:25,040 --> 01:01:27,760
data isn't actually alive but it does
1840
01:01:27,760 --> 01:01:29,760
have a life cycle
1841
01:01:29,760 --> 01:01:32,240
so how do data analysts bring data to
1842
01:01:32,240 --> 01:01:33,280
life
1843
01:01:33,280 --> 01:01:34,960
well it starts with the right data
1844
01:01:34,960 --> 01:01:37,040
analysis tool
1845
01:01:37,040 --> 01:01:39,760
these include spreadsheets databases
1846
01:01:39,760 --> 01:01:41,040
query languages
1847
01:01:41,040 --> 01:01:44,640
and visualization software don't worry
1848
01:01:44,640 --> 01:01:47,040
if you don't know how these work or even
1849
01:01:47,040 --> 01:01:48,480
what they are
1850
01:01:48,480 --> 01:01:51,040
at one point every data analyst has been
1851
01:01:51,040 --> 01:01:52,000
right where you are
1852
01:01:52,000 --> 01:01:55,119
right now and they probably had a lot of
1853
01:01:55,119 --> 01:01:57,520
the same questions
1854
01:01:57,520 --> 01:01:59,039
i remember when i first started learning
1855
01:01:59,039 --> 01:02:00,640
about spreadsheets
1856
01:02:00,640 --> 01:02:02,799
i was a young intern and the company i
1857
01:02:02,799 --> 01:02:03,760
was working for
1858
01:02:03,760 --> 01:02:05,440
was in the middle of a big systems
1859
01:02:05,440 --> 01:02:07,440
change that meant
1860
01:02:07,440 --> 01:02:09,760
we had to move tons of reports from the
1861
01:02:09,760 --> 01:02:10,559
old system
1862
01:02:10,559 --> 01:02:13,680
to the new one after a few weeks i
1863
01:02:13,680 --> 01:02:14,319
noticed that
1864
01:02:14,319 --> 01:02:16,480
even the people who are further in their
1865
01:02:16,480 --> 01:02:17,440
careers
1866
01:02:17,440 --> 01:02:20,720
were not as technically minded as i was
1867
01:02:20,720 --> 01:02:23,760
so that became a great opportunity for
1868
01:02:23,760 --> 01:02:25,280
me to add value
1869
01:02:25,280 --> 01:02:27,839
my aha spreadsheet moment came when i
1870
01:02:27,839 --> 01:02:29,440
started researching shortcuts
1871
01:02:29,440 --> 01:02:30,799
that i could use to work with the
1872
01:02:30,799 --> 01:02:32,640
spreadsheets more efficiently
1873
01:02:32,640 --> 01:02:34,480
this would really streamline the process
1874
01:02:34,480 --> 01:02:36,319
of getting those reports moved over to
1875
01:02:36,319 --> 01:02:37,599
the new system
1876
01:02:37,599 --> 01:02:39,520
once everything started flowing i
1877
01:02:39,520 --> 01:02:40,880
remember getting emails from other
1878
01:02:40,880 --> 01:02:42,799
finance analysts at the company
1879
01:02:42,799 --> 01:02:44,880
they were so grateful that someone had
1880
01:02:44,880 --> 01:02:46,640
come in and fixed a problem that no one
1881
01:02:46,640 --> 01:02:47,680
else could
1882
01:02:47,680 --> 01:02:49,680
that inspired me to go even further and
1883
01:02:49,680 --> 01:02:51,119
learn how to use spreadsheets
1884
01:02:51,119 --> 01:02:54,240
in all sorts of incredible ways as you
1885
01:02:54,240 --> 01:02:55,760
continue through this course
1886
01:02:55,760 --> 01:02:57,599
i bet you'll be just as impressed as i
1887
01:02:57,599 --> 01:02:59,440
was and before you know it
1888
01:02:59,440 --> 01:03:01,839
you'll bring data to life too let's get
1889
01:03:01,839 --> 01:03:04,390
started
1890
01:03:04,400 --> 01:03:06,480
here's a question for you when you think
1891
01:03:06,480 --> 01:03:07,920
about a life cycle
1892
01:03:07,920 --> 01:03:09,280
what's the first thing that comes to
1893
01:03:09,280 --> 01:03:11,359
mind now
1894
01:03:11,359 --> 01:03:13,119
i'm not a mind reader but i know
1895
01:03:13,119 --> 01:03:14,400
whatever you're thinking
1896
01:03:14,400 --> 01:03:17,200
is right there's actually no wrong
1897
01:03:17,200 --> 01:03:18,240
answer because
1898
01:03:18,240 --> 01:03:20,960
everything has a life cycle one of the
1899
01:03:20,960 --> 01:03:23,440
most well-known examples of a life cycle
1900
01:03:23,440 --> 01:03:26,880
is a butterfly butterflies begin as eggs
1901
01:03:26,880 --> 01:03:29,520
hatch into caterpillars and then become
1902
01:03:29,520 --> 01:03:31,119
a chrysalis
1903
01:03:31,119 --> 01:03:34,240
that's where the real magic happens data
1904
01:03:34,240 --> 01:03:36,480
has a life cycle of its own too
1905
01:03:36,480 --> 01:03:38,400
in this video we're going to talk about
1906
01:03:38,400 --> 01:03:39,520
each of the stages
1907
01:03:39,520 --> 01:03:41,280
in that life cycle to help you
1908
01:03:41,280 --> 01:03:43,200
understand the individual phases
1909
01:03:43,200 --> 01:03:46,400
data goes through the life cycle of data
1910
01:03:46,400 --> 01:03:50,400
is plan capture manage analyze
1911
01:03:50,400 --> 01:03:53,280
archive and destroy let's start with the
1912
01:03:53,280 --> 01:03:54,319
first phase
1913
01:03:54,319 --> 01:03:57,280
planning this actually happens well
1914
01:03:57,280 --> 01:03:59,760
before starting an analysis project
1915
01:03:59,760 --> 01:04:02,559
during planning a business decides what
1916
01:04:02,559 --> 01:04:04,240
kind of data it needs
1917
01:04:04,240 --> 01:04:05,680
how it will be managed throughout its
1918
01:04:05,680 --> 01:04:08,240
life cycle who will be responsible for
1919
01:04:08,240 --> 01:04:08,640
it
1920
01:04:08,640 --> 01:04:12,000
and the optimal outcomes for example
1921
01:04:12,000 --> 01:04:14,480
let's say an electricity provider wanted
1922
01:04:14,480 --> 01:04:15,119
to gain
1923
01:04:15,119 --> 01:04:17,920
insights into how to save people energy
1924
01:04:17,920 --> 01:04:19,200
in the planning phase
1925
01:04:19,200 --> 01:04:21,359
they might decide to capture information
1926
01:04:21,359 --> 01:04:23,200
on how much electricity its customers
1927
01:04:23,200 --> 01:04:24,559
use each year
1928
01:04:24,559 --> 01:04:26,160
what types of buildings are being
1929
01:04:26,160 --> 01:04:28,559
powered and what types of devices are
1930
01:04:28,559 --> 01:04:30,000
being powered inside of them
1931
01:04:30,000 --> 01:04:31,920
the electricity company would also
1932
01:04:31,920 --> 01:04:33,599
decide which team members will be
1933
01:04:33,599 --> 01:04:35,359
responsible for collecting
1934
01:04:35,359 --> 01:04:37,760
storing and sharing that data all of
1935
01:04:37,760 --> 01:04:39,359
this happens during planning
1936
01:04:39,359 --> 01:04:40,799
and it helps set up the rest of the
1937
01:04:40,799 --> 01:04:43,359
project the next phase
1938
01:04:43,359 --> 01:04:45,920
is when you capture data this is where
1939
01:04:45,920 --> 01:04:47,119
data is collected
1940
01:04:47,119 --> 01:04:49,760
from a variety of different sources and
1941
01:04:49,760 --> 01:04:51,599
brought into the organization
1942
01:04:51,599 --> 01:04:53,359
with so much data being created every
1943
01:04:53,359 --> 01:04:55,280
day the ways to collect it
1944
01:04:55,280 --> 01:04:58,240
are truly endless one common method is
1945
01:04:58,240 --> 01:05:00,480
getting data from outside resources
1946
01:05:00,480 --> 01:05:02,480
for example if you're doing data
1947
01:05:02,480 --> 01:05:04,240
analysis on weather patterns
1948
01:05:04,240 --> 01:05:06,000
you'd probably get data from a publicly
1949
01:05:06,000 --> 01:05:07,280
available data set
1950
01:05:07,280 --> 01:05:10,640
like the national climatic data center
1951
01:05:10,640 --> 01:05:12,319
another way to get data is from a
1952
01:05:12,319 --> 01:05:14,319
company's own documents and files
1953
01:05:14,319 --> 01:05:15,920
which are usually stored inside a
1954
01:05:15,920 --> 01:05:18,720
database while we've mentioned databases
1955
01:05:18,720 --> 01:05:19,680
before
1956
01:05:19,680 --> 01:05:21,680
we haven't gone into too much detail
1957
01:05:21,680 --> 01:05:23,359
about what they are
1958
01:05:23,359 --> 01:05:25,599
a database is a collection of data
1959
01:05:25,599 --> 01:05:27,680
stored in a computer system
1960
01:05:27,680 --> 01:05:30,319
in the case of our electricity provider
1961
01:05:30,319 --> 01:05:31,359
the business
1962
01:05:31,359 --> 01:05:33,680
would probably measure data usage among
1963
01:05:33,680 --> 01:05:34,559
its customers
1964
01:05:34,559 --> 01:05:37,359
within a database that it owns as a
1965
01:05:37,359 --> 01:05:38,160
quick note
1966
01:05:38,160 --> 01:05:40,000
when you maintain a database of customer
1967
01:05:40,000 --> 01:05:42,640
information ensuring data integrity
1968
01:05:42,640 --> 01:05:44,960
credibility and privacy are all
1969
01:05:44,960 --> 01:05:46,799
important concerns
1970
01:05:46,799 --> 01:05:48,839
you'll learn a lot more about that later
1971
01:05:48,839 --> 01:05:52,079
on now that we've captured our data
1972
01:05:52,079 --> 01:05:54,400
we move on to the next phase of the data
1973
01:05:54,400 --> 01:05:55,359
life cycle
1974
01:05:55,359 --> 01:05:57,680
manage here we're talking about how we
1975
01:05:57,680 --> 01:05:59,200
care for our data
1976
01:05:59,200 --> 01:06:01,839
how and where it's stored the tools used
1977
01:06:01,839 --> 01:06:03,599
to keep it safe and secure
1978
01:06:03,599 --> 01:06:05,760
and the actions taken to make sure that
1979
01:06:05,760 --> 01:06:07,599
it's maintained properly
1980
01:06:07,599 --> 01:06:09,760
this phase is very important to data
1981
01:06:09,760 --> 01:06:10,799
cleansing
1982
01:06:10,799 --> 01:06:14,319
which we'll cover later on next
1983
01:06:14,319 --> 01:06:16,799
it's time to analyze your data this is
1984
01:06:16,799 --> 01:06:18,000
where data analysts
1985
01:06:18,000 --> 01:06:21,119
really shine in this phase the data is
1986
01:06:21,119 --> 01:06:22,720
used to solve problems
1987
01:06:22,720 --> 01:06:24,400
make great decisions and support
1988
01:06:24,400 --> 01:06:26,000
business goals
1989
01:06:26,000 --> 01:06:28,240
for example one of our electricity
1990
01:06:28,240 --> 01:06:29,520
companies goals
1991
01:06:29,520 --> 01:06:31,440
might be to find ways to help customers
1992
01:06:31,440 --> 01:06:32,720
save energy
1993
01:06:32,720 --> 01:06:35,760
moving on the data lifecycle now evolves
1994
01:06:35,760 --> 01:06:38,799
to the archive phase archiving means
1995
01:06:38,799 --> 01:06:40,880
storing data in a place where it's still
1996
01:06:40,880 --> 01:06:44,000
available but may not be used again
1997
01:06:44,000 --> 01:06:46,720
during analysis analysts handle huge
1998
01:06:46,720 --> 01:06:48,319
amounts of data
1999
01:06:48,319 --> 01:06:50,079
can you imagine if we had to sort
2000
01:06:50,079 --> 01:06:51,839
through all of the available data that's
2001
01:06:51,839 --> 01:06:52,799
out there
2002
01:06:52,799 --> 01:06:55,200
even if it was no longer useful and
2003
01:06:55,200 --> 01:06:57,280
relevant to our work
2004
01:06:57,280 --> 01:06:59,520
it makes way more sense to archive it
2005
01:06:59,520 --> 01:07:01,039
than to keep it around
2006
01:07:01,039 --> 01:07:03,359
and finally the last step of the data
2007
01:07:03,359 --> 01:07:04,400
life cycle
2008
01:07:04,400 --> 01:07:08,000
the destroy phase yes it sounds sad
2009
01:07:08,000 --> 01:07:10,079
but when you destroy data it won't hurt
2010
01:07:10,079 --> 01:07:11,200
a bit
2011
01:07:11,200 --> 01:07:12,880
so let's get back to our electricity
2012
01:07:12,880 --> 01:07:14,799
provider example
2013
01:07:14,799 --> 01:07:16,799
they would have data stored on multiple
2014
01:07:16,799 --> 01:07:17,920
hard drives
2015
01:07:17,920 --> 01:07:19,920
to destroy it the company would use a
2016
01:07:19,920 --> 01:07:22,960
secure data erasure software
2017
01:07:22,960 --> 01:07:25,280
if there were any paper files they would
2018
01:07:25,280 --> 01:07:26,799
be shredded too
2019
01:07:26,799 --> 01:07:28,400
this is important for protecting a
2020
01:07:28,400 --> 01:07:30,079
company's private information
2021
01:07:30,079 --> 01:07:32,319
as well as private data about its
2022
01:07:32,319 --> 01:07:33,680
customers
2023
01:07:33,680 --> 01:07:35,839
and there you have it the data life
2024
01:07:35,839 --> 01:07:37,039
cycle
2025
01:07:37,039 --> 01:07:38,240
and now that you understand the
2026
01:07:38,240 --> 01:07:39,920
different phases data goes through
2027
01:07:39,920 --> 01:07:41,359
during its life cycle
2028
01:07:41,359 --> 01:07:43,200
you can better understand how to
2029
01:07:43,200 --> 01:07:45,920
approach the data analysis process
2030
01:07:45,920 --> 01:07:49,119
which we'll talk about soon
2031
01:07:49,119 --> 01:07:50,960
now that you understand all the phases
2032
01:07:50,960 --> 01:07:52,480
of the data life cycle
2033
01:07:52,480 --> 01:07:54,559
it's time to move on to the phases of
2034
01:07:54,559 --> 01:07:56,000
data analysis
2035
01:07:56,000 --> 01:07:58,480
they sound similar but are two different
2036
01:07:58,480 --> 01:07:59,599
things
2037
01:07:59,599 --> 01:08:02,400
data analysis isn't a life cycle it's
2038
01:08:02,400 --> 01:08:03,440
the process
2039
01:08:03,440 --> 01:08:06,240
of analyzing data coming up we'll look
2040
01:08:06,240 --> 01:08:06,960
at each
2041
01:08:06,960 --> 01:08:09,680
step of the data analysis process and
2042
01:08:09,680 --> 01:08:10,799
how it will relate
2043
01:08:10,799 --> 01:08:13,760
to your work as a data analyst even this
2044
01:08:13,760 --> 01:08:15,599
program is designed to follow these
2045
01:08:15,599 --> 01:08:16,400
steps
2046
01:08:16,400 --> 01:08:18,159
understanding these connections will
2047
01:08:18,159 --> 01:08:19,600
help guide your own
2048
01:08:19,600 --> 01:08:22,640
analysis and your work in this program
2049
01:08:22,640 --> 01:08:24,480
you've already learned that this program
2050
01:08:24,480 --> 01:08:26,400
is modeled after the stages
2051
01:08:26,400 --> 01:08:28,799
of the data analysis process this
2052
01:08:28,799 --> 01:08:30,960
program is split into courses
2053
01:08:30,960 --> 01:08:33,120
six of which are based upon the steps of
2054
01:08:33,120 --> 01:08:34,640
data analysis
2055
01:08:34,640 --> 01:08:37,759
ask prepare process
2056
01:08:37,759 --> 01:08:40,960
analyze share and act
2057
01:08:40,960 --> 01:08:43,279
okay let's start with the first step in
2058
01:08:43,279 --> 01:08:44,799
data analysis
2059
01:08:44,799 --> 01:08:48,400
the ass phase in this phase we do two
2060
01:08:48,400 --> 01:08:49,279
things
2061
01:08:49,279 --> 01:08:51,359
we define the problem to be solved and
2062
01:08:51,359 --> 01:08:53,440
we make sure that we fully understand
2063
01:08:53,440 --> 01:08:55,279
stakeholder expectations
2064
01:08:55,279 --> 01:08:57,920
stakeholders hold a stake in the project
2065
01:08:57,920 --> 01:08:58,799
there are people
2066
01:08:58,799 --> 01:09:00,960
who have invested time and resources
2067
01:09:00,960 --> 01:09:02,080
into our project
2068
01:09:02,080 --> 01:09:04,400
and are interested in the outcome let's
2069
01:09:04,400 --> 01:09:05,600
break that down
2070
01:09:05,600 --> 01:09:07,920
first defining a problem means you look
2071
01:09:07,920 --> 01:09:09,920
at the current state and identify how
2072
01:09:09,920 --> 01:09:10,560
it's different
2073
01:09:10,560 --> 01:09:13,040
from the ideal state usually there's an
2074
01:09:13,040 --> 01:09:15,120
obstacle we need to get rid of
2075
01:09:15,120 --> 01:09:16,880
or something wrong that needs to be
2076
01:09:16,880 --> 01:09:18,880
fixed for instance
2077
01:09:18,880 --> 01:09:20,960
a sports arena might want to reduce the
2078
01:09:20,960 --> 01:09:22,239
time fans spend
2079
01:09:22,239 --> 01:09:24,560
waiting in the ticket line the obstacle
2080
01:09:24,560 --> 01:09:25,520
is figuring out
2081
01:09:25,520 --> 01:09:27,839
how to get the customers to their seats
2082
01:09:27,839 --> 01:09:28,880
more quickly
2083
01:09:28,880 --> 01:09:31,199
another important part of the ass phase
2084
01:09:31,199 --> 01:09:32,560
is understanding
2085
01:09:32,560 --> 01:09:35,279
stakeholder expectations the first step
2086
01:09:35,279 --> 01:09:36,799
here is to determine
2087
01:09:36,799 --> 01:09:39,040
who the stakeholders are that may
2088
01:09:39,040 --> 01:09:40,960
include your manager
2089
01:09:40,960 --> 01:09:43,199
an executive sponsor or your sales
2090
01:09:43,199 --> 01:09:44,960
partners there can be lots of
2091
01:09:44,960 --> 01:09:46,319
stakeholders
2092
01:09:46,319 --> 01:09:48,400
but what they all have in common is that
2093
01:09:48,400 --> 01:09:50,080
they help make decisions
2094
01:09:50,080 --> 01:09:52,159
influence actions and strategies and
2095
01:09:52,159 --> 01:09:53,520
have specific goals
2096
01:09:53,520 --> 01:09:55,760
they want to meet they also care about
2097
01:09:55,760 --> 01:09:56,560
the project
2098
01:09:56,560 --> 01:09:58,000
and that's why it's so important to
2099
01:09:58,000 --> 01:10:00,080
understand their expectations
2100
01:10:00,080 --> 01:10:02,320
for instance if your manager assigns you
2101
01:10:02,320 --> 01:10:04,159
a data analysis project related to
2102
01:10:04,159 --> 01:10:05,199
business risk
2103
01:10:05,199 --> 01:10:06,960
it would be smart to confirm whether
2104
01:10:06,960 --> 01:10:09,040
they want to include all types of risks
2105
01:10:09,040 --> 01:10:10,719
that could affect the company
2106
01:10:10,719 --> 01:10:13,199
or just risks related to weather such as
2107
01:10:13,199 --> 01:10:15,120
hurricanes and tornadoes
2108
01:10:15,120 --> 01:10:16,640
communicating with your stakeholders is
2109
01:10:16,640 --> 01:10:18,640
key in making sure you stay engaged and
2110
01:10:18,640 --> 01:10:20,560
on track throughout the project
2111
01:10:20,560 --> 01:10:23,040
so as a data analyst developing strong
2112
01:10:23,040 --> 01:10:24,960
communication strategies is very
2113
01:10:24,960 --> 01:10:25,600
important
2114
01:10:25,600 --> 01:10:27,840
this part of the ask phase helps you
2115
01:10:27,840 --> 01:10:28,719
keep focused
2116
01:10:28,719 --> 01:10:31,120
on the problem itself not just its
2117
01:10:31,120 --> 01:10:32,080
symptoms
2118
01:10:32,080 --> 01:10:34,719
as you learned earlier the five why's
2119
01:10:34,719 --> 01:10:37,120
are extremely helpful here
2120
01:10:37,120 --> 01:10:39,360
in an upcoming course you'll learn how
2121
01:10:39,360 --> 01:10:41,199
to ask effective questions
2122
01:10:41,199 --> 01:10:43,199
and define the problem by working with
2123
01:10:43,199 --> 01:10:44,640
stakeholders
2124
01:10:44,640 --> 01:10:46,880
you'll also cover strategies that can
2125
01:10:46,880 --> 01:10:47,840
help you share
2126
01:10:47,840 --> 01:10:49,760
what you discover in a way that keeps
2127
01:10:49,760 --> 01:10:50,880
people interested
2128
01:10:50,880 --> 01:10:53,199
after that we'll move on to the prepare
2129
01:10:53,199 --> 01:10:55,840
step of the data analysis process
2130
01:10:55,840 --> 01:10:57,920
this is where data analysts collect and
2131
01:10:57,920 --> 01:10:58,880
store data
2132
01:10:58,880 --> 01:11:00,880
they'll use for the upcoming analysis
2133
01:11:00,880 --> 01:11:03,040
process you'll learn more about the
2134
01:11:03,040 --> 01:11:04,480
different types of data
2135
01:11:04,480 --> 01:11:06,640
and how to identify which kinds of data
2136
01:11:06,640 --> 01:11:07,760
are most useful
2137
01:11:07,760 --> 01:11:10,239
for solving a particular problem you'll
2138
01:11:10,239 --> 01:11:12,239
also discover why it's so important
2139
01:11:12,239 --> 01:11:14,400
that your data and results are objective
2140
01:11:14,400 --> 01:11:15,760
and unbiased
2141
01:11:15,760 --> 01:11:18,320
in other words any decisions made from
2142
01:11:18,320 --> 01:11:19,280
your analysis
2143
01:11:19,280 --> 01:11:21,840
should always be based on facts and be
2144
01:11:21,840 --> 01:11:22,400
fair and
2145
01:11:22,400 --> 01:11:26,480
impartial next is the process step here
2146
01:11:26,480 --> 01:11:28,719
data analysts find and eliminate any
2147
01:11:28,719 --> 01:11:30,400
errors and inaccuracies
2148
01:11:30,400 --> 01:11:32,560
that can get in the way of results this
2149
01:11:32,560 --> 01:11:34,640
usually means cleaning data
2150
01:11:34,640 --> 01:11:37,360
transforming it into more useful format
2151
01:11:37,360 --> 01:11:39,440
combining two or more data sets to make
2152
01:11:39,440 --> 01:11:41,679
information more complete and removing
2153
01:11:41,679 --> 01:11:42,560
outliers
2154
01:11:42,560 --> 01:11:44,320
which are any data points that could
2155
01:11:44,320 --> 01:11:46,080
skew the information
2156
01:11:46,080 --> 01:11:47,840
after that you'll learn how to check the
2157
01:11:47,840 --> 01:11:49,520
data you prepared to make sure it's
2158
01:11:49,520 --> 01:11:50,880
complete and correct
2159
01:11:50,880 --> 01:11:52,480
this phase is all about getting the
2160
01:11:52,480 --> 01:11:54,560
details right so you'll also fix
2161
01:11:54,560 --> 01:11:57,280
typos inconsistencies or missing in
2162
01:11:57,280 --> 01:11:58,800
inaccurate data
2163
01:11:58,800 --> 01:12:01,440
and to top it off you'll gain strategies
2164
01:12:01,440 --> 01:12:03,199
for verifying and sharing your data
2165
01:12:03,199 --> 01:12:05,280
cleansing with stakeholders
2166
01:12:05,280 --> 01:12:08,400
then it's time to analyze analyzing the
2167
01:12:08,400 --> 01:12:10,159
data you've collected involves using
2168
01:12:10,159 --> 01:12:11,520
tools to transform and
2169
01:12:11,520 --> 01:12:13,520
organize that information so that you
2170
01:12:13,520 --> 01:12:15,199
can draw useful conclusions
2171
01:12:15,199 --> 01:12:17,199
make predictions and drive informed
2172
01:12:17,199 --> 01:12:18,239
decision making
2173
01:12:18,239 --> 01:12:20,080
there are lots of powerful tools data
2174
01:12:20,080 --> 01:12:21,679
analysts use in their work
2175
01:12:21,679 --> 01:12:23,520
and in this course you'll learn about
2176
01:12:23,520 --> 01:12:25,280
two of them spreadsheets
2177
01:12:25,280 --> 01:12:28,640
and structure query language or sql
2178
01:12:28,640 --> 01:12:31,600
which is often pronounced sql the next
2179
01:12:31,600 --> 01:12:33,760
course is based on the share phase
2180
01:12:33,760 --> 01:12:35,920
here you'll learn how data analysts
2181
01:12:35,920 --> 01:12:37,120
interpret results
2182
01:12:37,120 --> 01:12:39,040
and share them with others to help
2183
01:12:39,040 --> 01:12:41,040
stakeholders make effective
2184
01:12:41,040 --> 01:12:43,440
data-driven decisions in the shared
2185
01:12:43,440 --> 01:12:44,239
phase
2186
01:12:44,239 --> 01:12:46,880
visualization is a data analyst's best
2187
01:12:46,880 --> 01:12:47,360
friend
2188
01:12:47,360 --> 01:12:49,040
so this course will highlight why
2189
01:12:49,040 --> 01:12:50,800
visualization is essential
2190
01:12:50,800 --> 01:12:52,719
to getting others to understand what
2191
01:12:52,719 --> 01:12:54,000
your data is telling you
2192
01:12:54,000 --> 01:12:56,560
with the right visuals facts and figures
2193
01:12:56,560 --> 01:12:58,480
become so much easier to see
2194
01:12:58,480 --> 01:13:00,880
and complex concepts become easier to
2195
01:13:00,880 --> 01:13:01,840
understand
2196
01:13:01,840 --> 01:13:03,760
we'll explore different kinds of visuals
2197
01:13:03,760 --> 01:13:06,400
and some great data visualization tools
2198
01:13:06,400 --> 01:13:07,840
you'll also practice your own
2199
01:13:07,840 --> 01:13:09,440
presentation skills by creating
2200
01:13:09,440 --> 01:13:10,960
compelling slide shows
2201
01:13:10,960 --> 01:13:13,280
and learning how to be fully prepared to
2202
01:13:13,280 --> 01:13:14,640
answer questions
2203
01:13:14,640 --> 01:13:16,159
then we'll take a break from the data
2204
01:13:16,159 --> 01:13:18,080
analysis process to show you all
2205
01:13:18,080 --> 01:13:20,080
of the really cool things you can do
2206
01:13:20,080 --> 01:13:21,440
with the programming language
2207
01:13:21,440 --> 01:13:24,480
r you don't need to be familiar with r
2208
01:13:24,480 --> 01:13:27,199
or programming languages in general just
2209
01:13:27,199 --> 01:13:27,920
know that r
2210
01:13:27,920 --> 01:13:30,560
is a popular tool for data manipulation
2211
01:13:30,560 --> 01:13:31,679
calculation
2212
01:13:31,679 --> 01:13:34,800
and visualization and for our final data
2213
01:13:34,800 --> 01:13:35,920
analysis phase
2214
01:13:35,920 --> 01:13:38,640
we have act this is the exciting moment
2215
01:13:38,640 --> 01:13:39,760
when the business takes
2216
01:13:39,760 --> 01:13:41,840
all of the insights you the data
2217
01:13:41,840 --> 01:13:43,440
analysts have provided
2218
01:13:43,440 --> 01:13:45,360
and puts them to work in order to solve
2219
01:13:45,360 --> 01:13:47,040
the original business problem
2220
01:13:47,040 --> 01:13:48,719
and will be acting on what you've
2221
01:13:48,719 --> 01:13:50,880
learned throughout this program
2222
01:13:50,880 --> 01:13:52,719
this is when you'll prepare for your job
2223
01:13:52,719 --> 01:13:54,800
search and have the chance to complete
2224
01:13:54,800 --> 01:13:57,120
a case study project it's a great
2225
01:13:57,120 --> 01:13:57,840
opportunity
2226
01:13:57,840 --> 01:13:59,360
for you to bring together everything
2227
01:13:59,360 --> 01:14:01,840
you've worked on throughout this course
2228
01:14:01,840 --> 01:14:03,920
plus adding a case study to your
2229
01:14:03,920 --> 01:14:05,840
portfolio helps you stand out from the
2230
01:14:05,840 --> 01:14:06,640
other candidates
2231
01:14:06,640 --> 01:14:08,320
when you interview for your first data
2232
01:14:08,320 --> 01:14:09,679
analyst job
2233
01:14:09,679 --> 01:14:11,120
now you know the different steps of the
2234
01:14:11,120 --> 01:14:12,800
data analysis process
2235
01:14:12,800 --> 01:14:15,199
and how our course reflects it you have
2236
01:14:15,199 --> 01:14:17,280
everything you need to understand
2237
01:14:17,280 --> 01:14:19,600
how this course works and my fellow
2238
01:14:19,600 --> 01:14:20,640
googlers and i
2239
01:14:20,640 --> 01:14:22,800
will be here to guide you every step of
2240
01:14:22,800 --> 01:14:28,480
the way
2241
01:14:28,480 --> 01:14:30,800
regardless of what type of data analysis
2242
01:14:30,800 --> 01:14:32,400
you're conducting
2243
01:14:32,400 --> 01:14:34,560
the process is generally the same the
2244
01:14:34,560 --> 01:14:36,159
example that i'll walk through
2245
01:14:36,159 --> 01:14:37,840
is that of our employee engagement
2246
01:14:37,840 --> 01:14:39,600
survey but you can imagine that this
2247
01:14:39,600 --> 01:14:41,520
process applies to just about any data
2248
01:14:41,520 --> 01:14:42,000
analysis
2249
01:14:42,000 --> 01:14:43,280
that you're going to conduct as an
2250
01:14:43,280 --> 01:14:45,360
analyst the first thing you want to do
2251
01:14:45,360 --> 01:14:45,600
is
2252
01:14:45,600 --> 01:14:48,320
ask you want to ask all of the right
2253
01:14:48,320 --> 01:14:50,239
questions at the beginning of the
2254
01:14:50,239 --> 01:14:51,120
engagement
2255
01:14:51,120 --> 01:14:53,760
so you better understand what your
2256
01:14:53,760 --> 01:14:55,679
leaders and stakeholders need from this
2257
01:14:55,679 --> 01:14:56,960
analysis
2258
01:14:56,960 --> 01:14:58,400
so the types of questions that i
2259
01:14:58,400 --> 01:15:00,400
generally ask are around
2260
01:15:00,400 --> 01:15:02,400
you know what is the problem that we're
2261
01:15:02,400 --> 01:15:03,920
trying to solve
2262
01:15:03,920 --> 01:15:06,480
what is the purpose of this analysis
2263
01:15:06,480 --> 01:15:07,840
what are we hoping to learn
2264
01:15:07,840 --> 01:15:09,920
from it so after you've asked all the
2265
01:15:09,920 --> 01:15:11,280
right questions and you've
2266
01:15:11,280 --> 01:15:12,719
wrapped your arms around the scope of
2267
01:15:12,719 --> 01:15:14,719
the analysis you need to conduct
2268
01:15:14,719 --> 01:15:17,360
the next step is to prepare we need to
2269
01:15:17,360 --> 01:15:18,480
be thinking about
2270
01:15:18,480 --> 01:15:20,400
what type of data we need to answer
2271
01:15:20,400 --> 01:15:22,159
those key questions this could be
2272
01:15:22,159 --> 01:15:23,520
anything from
2273
01:15:23,520 --> 01:15:25,920
quantitative data or qualitative data it
2274
01:15:25,920 --> 01:15:26,960
could be
2275
01:15:26,960 --> 01:15:29,520
cross-sectional or point in time versus
2276
01:15:29,520 --> 01:15:30,880
longitudinal over
2277
01:15:30,880 --> 01:15:33,280
a long period of time we need to be
2278
01:15:33,280 --> 01:15:35,280
thinking about the type of data we need
2279
01:15:35,280 --> 01:15:36,880
in order to answer the questions that
2280
01:15:36,880 --> 01:15:38,640
we've set out to answer
2281
01:15:38,640 --> 01:15:40,159
based on what we learned when we asked
2282
01:15:40,159 --> 01:15:42,000
the right questions
2283
01:15:42,000 --> 01:15:43,760
we also need to be thinking about how
2284
01:15:43,760 --> 01:15:45,760
we're going to collect that data
2285
01:15:45,760 --> 01:15:48,000
or if we need to collect that data it
2286
01:15:48,000 --> 01:15:48,960
may be the case
2287
01:15:48,960 --> 01:15:51,280
that we need to collect this data brand
2288
01:15:51,280 --> 01:15:53,120
new and so we need to think about what
2289
01:15:53,120 --> 01:15:54,320
type of data we're going to be
2290
01:15:54,320 --> 01:15:55,840
collecting and how
2291
01:15:55,840 --> 01:15:58,239
for our employee engagement survey we do
2292
01:15:58,239 --> 01:16:00,560
that via a survey of both quantitative
2293
01:16:00,560 --> 01:16:03,280
and qualitative questions but it may
2294
01:16:03,280 --> 01:16:04,560
actually be the case that for many
2295
01:16:04,560 --> 01:16:05,600
analyses
2296
01:16:05,600 --> 01:16:07,440
the data that you're looking for already
2297
01:16:07,440 --> 01:16:08,719
exists
2298
01:16:08,719 --> 01:16:10,080
then it's a question of working with
2299
01:16:10,080 --> 01:16:11,679
those data owners to make sure that
2300
01:16:11,679 --> 01:16:13,280
you're able to leverage that data and
2301
01:16:13,280 --> 01:16:15,120
use it responsibly
2302
01:16:15,120 --> 01:16:16,880
after you've done all the hard work to
2303
01:16:16,880 --> 01:16:18,400
collect your data
2304
01:16:18,400 --> 01:16:21,120
now you need to process that data it
2305
01:16:21,120 --> 01:16:22,880
begins with cleaning
2306
01:16:22,880 --> 01:16:25,679
this to me is the most fun part of the
2307
01:16:25,679 --> 01:16:27,440
data analytics process
2308
01:16:27,440 --> 01:16:29,280
you know we can think of it as the
2309
01:16:29,280 --> 01:16:31,920
initial introduction or the handshake
2310
01:16:31,920 --> 01:16:33,920
you know hello to your data this is
2311
01:16:33,920 --> 01:16:36,000
where you get a chance to understand
2312
01:16:36,000 --> 01:16:39,199
its structure its quirks its nuances
2313
01:16:39,199 --> 01:16:40,800
and you really get a chance to
2314
01:16:40,800 --> 01:16:42,560
understand deeply
2315
01:16:42,560 --> 01:16:44,000
what type of data you're going to be
2316
01:16:44,000 --> 01:16:46,159
working with and understanding what
2317
01:16:46,159 --> 01:16:48,080
potential that data has to answer
2318
01:16:48,080 --> 01:16:50,560
all of your questions this is such an
2319
01:16:50,560 --> 01:16:51,360
important part
2320
01:16:51,360 --> 01:16:53,520
too where we're running through all of
2321
01:16:53,520 --> 01:16:55,679
our quality assurance checks
2322
01:16:55,679 --> 01:16:58,159
for example do we have all of the data
2323
01:16:58,159 --> 01:17:00,719
that we anticipated we would have
2324
01:17:00,719 --> 01:17:03,120
are we missing data at random or is it
2325
01:17:03,120 --> 01:17:05,040
missing in a systematic way
2326
01:17:05,040 --> 01:17:06,560
such that maybe something went wrong
2327
01:17:06,560 --> 01:17:08,719
with our data collection effort
2328
01:17:08,719 --> 01:17:10,800
if needed did we code all of our data
2329
01:17:10,800 --> 01:17:12,159
the right way
2330
01:17:12,159 --> 01:17:14,080
are there any outliers that we need to
2331
01:17:14,080 --> 01:17:15,520
treat differently
2332
01:17:15,520 --> 01:17:16,960
you know this is the part where you
2333
01:17:16,960 --> 01:17:19,120
spend a lot of time
2334
01:17:19,120 --> 01:17:21,840
really digging deeply into the structure
2335
01:17:21,840 --> 01:17:23,360
and nuance of the data
2336
01:17:23,360 --> 01:17:25,280
to make sure that you're able to analyze
2337
01:17:25,280 --> 01:17:27,679
it appropriately and responsibly
2338
01:17:27,679 --> 01:17:29,520
after cleaning our data and running all
2339
01:17:29,520 --> 01:17:30,800
of our quality assurance
2340
01:17:30,800 --> 01:17:33,360
checks now is the point where we analyze
2341
01:17:33,360 --> 01:17:34,560
our data
2342
01:17:34,560 --> 01:17:37,120
making sure to do so in as objective and
2343
01:17:37,120 --> 01:17:39,520
unbiased a way as possible
2344
01:17:39,520 --> 01:17:41,679
to do this the first thing we do is run
2345
01:17:41,679 --> 01:17:43,920
through a series of analyses that we've
2346
01:17:43,920 --> 01:17:46,000
already planned ahead of time
2347
01:17:46,000 --> 01:17:47,679
based on the questions that we know we
2348
01:17:47,679 --> 01:17:49,360
want to answer from the very very
2349
01:17:49,360 --> 01:17:50,960
beginning of the process
2350
01:17:50,960 --> 01:17:52,480
one thing that's probably the hardest
2351
01:17:52,480 --> 01:17:54,239
about this particular process the
2352
01:17:54,239 --> 01:17:56,320
hardest thing about analyzing data
2353
01:17:56,320 --> 01:17:58,880
is that we as analysts are are trained
2354
01:17:58,880 --> 01:17:59,520
to look for
2355
01:17:59,520 --> 01:18:02,640
patterns and over time as we become
2356
01:18:02,640 --> 01:18:04,480
better and better at our jobs
2357
01:18:04,480 --> 01:18:06,000
what we'll often find is that we can
2358
01:18:06,000 --> 01:18:08,239
start to intuit what we might see in the
2359
01:18:08,239 --> 01:18:08,960
data
2360
01:18:08,960 --> 01:18:10,800
we might have a sneaking suspicion as to
2361
01:18:10,800 --> 01:18:12,000
what the data are going to tell
2362
01:18:12,000 --> 01:18:13,920
us and this is the point where we have
2363
01:18:13,920 --> 01:18:15,679
to take a step back
2364
01:18:15,679 --> 01:18:18,400
and let the data speak for itself you
2365
01:18:18,400 --> 01:18:19,800
know as data analysts we are
2366
01:18:19,800 --> 01:18:21,199
storytellers
2367
01:18:21,199 --> 01:18:22,960
but we also have to keep in mind that it
2368
01:18:22,960 --> 01:18:25,040
is not our story to tell
2369
01:18:25,040 --> 01:18:27,679
that story belongs to the data and it is
2370
01:18:27,679 --> 01:18:29,120
our job as analysts
2371
01:18:29,120 --> 01:18:32,320
to amplify and tell that story in as
2372
01:18:32,320 --> 01:18:34,640
unbiased and objective a way as possible
2373
01:18:34,640 --> 01:18:36,640
the next step is to share all of the
2374
01:18:36,640 --> 01:18:38,400
data and insights that you've generated
2375
01:18:38,400 --> 01:18:40,880
from your analyses now typically for our
2376
01:18:40,880 --> 01:18:42,560
employee engagement survey
2377
01:18:42,560 --> 01:18:44,080
we start by sharing the high level
2378
01:18:44,080 --> 01:18:46,239
findings with our executive team
2379
01:18:46,239 --> 01:18:47,920
we want them to have a landscape view of
2380
01:18:47,920 --> 01:18:50,000
how the organization is feeling
2381
01:18:50,000 --> 01:18:51,040
and we want to make sure that there
2382
01:18:51,040 --> 01:18:53,360
aren't any surprises as they dig deeper
2383
01:18:53,360 --> 01:18:54,719
and deeper into the data
2384
01:18:54,719 --> 01:18:57,199
to understand how teams are feeling and
2385
01:18:57,199 --> 01:18:59,600
how individual employees are feeling
2386
01:18:59,600 --> 01:19:02,719
all of this work from asking the right
2387
01:19:02,719 --> 01:19:04,800
questions to collecting your data
2388
01:19:04,800 --> 01:19:07,520
to analyzing and sharing doesn't mean
2389
01:19:07,520 --> 01:19:08,000
much of
2390
01:19:08,000 --> 01:19:10,320
anything if we aren't taking action on
2391
01:19:10,320 --> 01:19:12,480
what we've just learned
2392
01:19:12,480 --> 01:19:15,120
this to me is the most critical part
2393
01:19:15,120 --> 01:19:17,040
especially of our employee engagement
2394
01:19:17,040 --> 01:19:18,000
survey
2395
01:19:18,000 --> 01:19:19,440
i like to say that the survey is
2396
01:19:19,440 --> 01:19:21,040
actually the easy part
2397
01:19:21,040 --> 01:19:22,800
and acting on the results is really
2398
01:19:22,800 --> 01:19:25,280
where the real work begins
2399
01:19:25,280 --> 01:19:26,800
this is where we use all of those
2400
01:19:26,800 --> 01:19:29,199
data-driven insights to decide
2401
01:19:29,199 --> 01:19:30,880
what types of interventions we want to
2402
01:19:30,880 --> 01:19:33,199
introduce not only the organizational
2403
01:19:33,199 --> 01:19:36,159
level but also at the team level as well
2404
01:19:36,159 --> 01:19:38,080
so we might find for example that the
2405
01:19:38,080 --> 01:19:40,320
organization is working on a series of
2406
01:19:40,320 --> 01:19:42,239
of interventions to help
2407
01:19:42,239 --> 01:19:44,640
improve part of the employee experience
2408
01:19:44,640 --> 01:19:46,560
whereas individual teams have additional
2409
01:19:46,560 --> 01:19:46,960
roles
2410
01:19:46,960 --> 01:19:50,000
responsibilities to play to either
2411
01:19:50,000 --> 01:19:52,159
bolster some of those efforts or to
2412
01:19:52,159 --> 01:19:54,239
introduce new ones to sort of better
2413
01:19:54,239 --> 01:19:55,679
meet their team where where their
2414
01:19:55,679 --> 01:19:57,920
strengths and opportunity areas are
2415
01:19:57,920 --> 01:20:01,440
the data analysis process is rigorous
2416
01:20:01,440 --> 01:20:04,320
but it is lengthy and i can completely
2417
01:20:04,320 --> 01:20:05,600
appreciate
2418
01:20:05,600 --> 01:20:08,960
that we as data analysts get so excited
2419
01:20:08,960 --> 01:20:12,400
about just diving right into the data
2420
01:20:12,400 --> 01:20:15,840
and doing what we do best the challenge
2421
01:20:15,840 --> 01:20:17,120
is that if we don't
2422
01:20:17,120 --> 01:20:18,960
work through the process and in its
2423
01:20:18,960 --> 01:20:20,800
entirety if we try to skip
2424
01:20:20,800 --> 01:20:23,120
steps we're not going to be able to
2425
01:20:23,120 --> 01:20:24,000
elicit the
2426
01:20:24,000 --> 01:20:26,560
the insights that we're looking for i
2427
01:20:26,560 --> 01:20:27,760
absolutely love my job
2428
01:20:27,760 --> 01:20:31,280
i i such a deep appreciation for
2429
01:20:31,280 --> 01:20:34,159
for data and what it can do and and what
2430
01:20:34,159 --> 01:20:35,760
type of insight we can derive
2431
01:20:35,760 --> 01:20:39,669
from it
2432
01:20:39,679 --> 01:20:41,280
i'm looking forward to introducing you
2433
01:20:41,280 --> 01:20:43,120
to some of the tools data analysts use
2434
01:20:43,120 --> 01:20:44,639
each and every day
2435
01:20:44,639 --> 01:20:46,480
there are tons of options out there but
2436
01:20:46,480 --> 01:20:48,320
the most common ones you'll see analysts
2437
01:20:48,320 --> 01:20:48,639
use
2438
01:20:48,639 --> 01:20:50,880
are spreadsheets query languages and
2439
01:20:50,880 --> 01:20:52,400
visualization tools
2440
01:20:52,400 --> 01:20:54,560
and this video is going to give you a
2441
01:20:54,560 --> 01:20:57,040
quick look at how these tools are being
2442
01:20:57,040 --> 01:20:58,639
used by data analysts
2443
01:20:58,639 --> 01:21:00,960
every day believe it or not i was
2444
01:21:00,960 --> 01:21:02,639
several years into my accounting and
2445
01:21:02,639 --> 01:21:04,080
finance career before i saw
2446
01:21:04,080 --> 01:21:06,639
all of these tools working together at
2447
01:21:06,639 --> 01:21:07,679
that point
2448
01:21:07,679 --> 01:21:09,760
i was very experienced with spreadsheets
2449
01:21:09,760 --> 01:21:11,520
and had worked in large data sets
2450
01:21:11,520 --> 01:21:13,360
with some of the traditional database
2451
01:21:13,360 --> 01:21:14,719
programs
2452
01:21:14,719 --> 01:21:17,120
i had the foundational skill set to use
2453
01:21:17,120 --> 01:21:18,320
query languages
2454
01:21:18,320 --> 01:21:21,440
and i had dabbled in visualizations but
2455
01:21:21,440 --> 01:21:23,920
i had never brought them all together
2456
01:21:23,920 --> 01:21:26,320
then i got hired here at google and it
2457
01:21:26,320 --> 01:21:28,639
was so eye-opening to come into a place
2458
01:21:28,639 --> 01:21:29,440
like this
2459
01:21:29,440 --> 01:21:31,840
with an abundance of information
2460
01:21:31,840 --> 01:21:33,040
everywhere you look
2461
01:21:33,040 --> 01:21:35,280
as an analyst at google the true power
2462
01:21:35,280 --> 01:21:36,400
of these tools
2463
01:21:36,400 --> 01:21:39,280
became so much clearer to me i became
2464
01:21:39,280 --> 01:21:41,199
more focused on really maximizing
2465
01:21:41,199 --> 01:21:42,960
everything these tools could do
2466
01:21:42,960 --> 01:21:44,960
streamlining my reporting and just
2467
01:21:44,960 --> 01:21:46,800
making my work simpler
2468
01:21:46,800 --> 01:21:48,800
all of a sudden i had a lot more time
2469
01:21:48,800 --> 01:21:51,520
and space to dedicate to identifying new
2470
01:21:51,520 --> 01:21:52,719
problems to solve
2471
01:21:52,719 --> 01:21:55,440
and driving decision making without a
2472
01:21:55,440 --> 01:21:56,159
doubt
2473
01:21:56,159 --> 01:21:57,600
once you've learned the power of these
2474
01:21:57,600 --> 01:21:59,600
tools you will be well on your way to
2475
01:21:59,600 --> 01:22:01,360
becoming the best data analyst you can
2476
01:22:01,360 --> 01:22:02,560
possibly be
2477
01:22:02,560 --> 01:22:04,639
alright i hope that story has you even
2478
01:22:04,639 --> 01:22:05,679
more motivated
2479
01:22:05,679 --> 01:22:07,760
for this course let's get started with
2480
01:22:07,760 --> 01:22:09,040
spreadsheets
2481
01:22:09,040 --> 01:22:10,800
again there are lots of different
2482
01:22:10,800 --> 01:22:12,159
spreadsheet solutions
2483
01:22:12,159 --> 01:22:14,880
but two popular options are microsoft
2484
01:22:14,880 --> 01:22:15,520
excel
2485
01:22:15,520 --> 01:22:18,000
and google sheets to put it simply a
2486
01:22:18,000 --> 01:22:18,719
spreadsheet
2487
01:22:18,719 --> 01:22:21,199
is a digital worksheet it stores
2488
01:22:21,199 --> 01:22:22,000
organizes
2489
01:22:22,000 --> 01:22:24,880
and sorts data this is important because
2490
01:22:24,880 --> 01:22:26,400
the usefulness of your data
2491
01:22:26,400 --> 01:22:28,960
depends on how well it's structured when
2492
01:22:28,960 --> 01:22:30,960
you put your data into a spreadsheet
2493
01:22:30,960 --> 01:22:33,520
you can see patterns group informations
2494
01:22:33,520 --> 01:22:36,239
and easily find the information you need
2495
01:22:36,239 --> 01:22:37,840
spreadsheets also have some really
2496
01:22:37,840 --> 01:22:39,920
useful features called formulas and
2497
01:22:39,920 --> 01:22:40,880
functions
2498
01:22:40,880 --> 01:22:43,600
a formula is a set of instructions that
2499
01:22:43,600 --> 01:22:45,760
performs a specific calculation
2500
01:22:45,760 --> 01:22:48,560
using the data in a spreadsheet formulas
2501
01:22:48,560 --> 01:22:50,560
can do basic things like add
2502
01:22:50,560 --> 01:22:53,440
subtract multiply and divide but they
2503
01:22:53,440 --> 01:22:54,960
don't stop there
2504
01:22:54,960 --> 01:22:57,360
you can also use formulas to find the
2505
01:22:57,360 --> 01:22:58,880
average of a number set
2506
01:22:58,880 --> 01:23:01,280
look up a particular value return the
2507
01:23:01,280 --> 01:23:03,360
sum of a set of values that meets a
2508
01:23:03,360 --> 01:23:04,639
particular rule
2509
01:23:04,639 --> 01:23:07,920
and so much more a function is a preset
2510
01:23:07,920 --> 01:23:08,560
command
2511
01:23:08,560 --> 01:23:10,880
that automatically performs a specific
2512
01:23:10,880 --> 01:23:11,760
process
2513
01:23:11,760 --> 01:23:13,520
or task using the data in the
2514
01:23:13,520 --> 01:23:14,880
spreadsheet
2515
01:23:14,880 --> 01:23:17,520
that sounds pretty technical i know so
2516
01:23:17,520 --> 01:23:18,719
let's break it down
2517
01:23:18,719 --> 01:23:20,880
just think of a function as a simpler
2518
01:23:20,880 --> 01:23:22,560
more efficient way of doing
2519
01:23:22,560 --> 01:23:24,480
something that would normally take a lot
2520
01:23:24,480 --> 01:23:26,800
of time in other words
2521
01:23:26,800 --> 01:23:28,239
functions can help make you more
2522
01:23:28,239 --> 01:23:30,400
efficient those are the spreadsheet
2523
01:23:30,400 --> 01:23:32,239
basics for now
2524
01:23:32,239 --> 01:23:35,120
later on you'll see them in action and
2525
01:23:35,120 --> 01:23:37,440
start working with spreadsheets yourself
2526
01:23:37,440 --> 01:23:39,600
the next data analysis tool is called
2527
01:23:39,600 --> 01:23:40,639
query language
2528
01:23:40,639 --> 01:23:42,320
a query language is a computer
2529
01:23:42,320 --> 01:23:44,480
programming language that allows you to
2530
01:23:44,480 --> 01:23:46,400
retrieve and manipulate data from a
2531
01:23:46,400 --> 01:23:47,520
database
2532
01:23:47,520 --> 01:23:49,120
you'll learn something called structured
2533
01:23:49,120 --> 01:23:51,840
query language more commonly known
2534
01:23:51,840 --> 01:23:55,199
as sql sql is a language that lets data
2535
01:23:55,199 --> 01:23:56,480
analysts communicate
2536
01:23:56,480 --> 01:23:58,719
with a database a database is a
2537
01:23:58,719 --> 01:24:00,880
collection of data stored in a computer
2538
01:24:00,880 --> 01:24:03,520
system sql is the most widely used
2539
01:24:03,520 --> 01:24:05,040
structured query language
2540
01:24:05,040 --> 01:24:07,520
for a couple of reasons it's easy to
2541
01:24:07,520 --> 01:24:08,719
understand and works
2542
01:24:08,719 --> 01:24:11,760
very well with all kinds of databases
2543
01:24:11,760 --> 01:24:14,560
with sql data analysts can access the
2544
01:24:14,560 --> 01:24:15,520
data they need
2545
01:24:15,520 --> 01:24:18,159
by making the query although query means
2546
01:24:18,159 --> 01:24:19,040
question
2547
01:24:19,040 --> 01:24:20,400
i like to think of it as more of a
2548
01:24:20,400 --> 01:24:23,440
request so you're requesting that the
2549
01:24:23,440 --> 01:24:26,000
database do something for you
2550
01:24:26,000 --> 01:24:27,920
you can ask it to do a lot of different
2551
01:24:27,920 --> 01:24:29,120
things such as
2552
01:24:29,120 --> 01:24:33,199
insert delete select or update data
2553
01:24:33,199 --> 01:24:36,400
okay that's a top level look at sql
2554
01:24:36,400 --> 01:24:38,239
in a later video we'll explore it
2555
01:24:38,239 --> 01:24:40,480
further and use sql to do some really
2556
01:24:40,480 --> 01:24:42,320
cool things with data
2557
01:24:42,320 --> 01:24:43,920
lastly let's talk about data
2558
01:24:43,920 --> 01:24:45,840
visualization you've learned that data
2559
01:24:45,840 --> 01:24:47,440
visualization is the graphical
2560
01:24:47,440 --> 01:24:49,520
representation of information
2561
01:24:49,520 --> 01:24:52,320
some examples include graphs maps and
2562
01:24:52,320 --> 01:24:53,440
tables
2563
01:24:53,440 --> 01:24:55,760
most people process visuals more easily
2564
01:24:55,760 --> 01:24:57,120
than words alone
2565
01:24:57,120 --> 01:24:58,880
that's why visualizations are so
2566
01:24:58,880 --> 01:25:00,639
important they help data analysts
2567
01:25:00,639 --> 01:25:02,560
communicate their insights to others
2568
01:25:02,560 --> 01:25:05,440
in an effective and compelling way when
2569
01:25:05,440 --> 01:25:07,120
you think about the data analysis
2570
01:25:07,120 --> 01:25:08,000
process
2571
01:25:08,000 --> 01:25:10,560
after data is prepared processed and
2572
01:25:10,560 --> 01:25:11,440
analyzed
2573
01:25:11,440 --> 01:25:14,000
the insights are visualized so it can be
2574
01:25:14,000 --> 01:25:15,440
understood and shared
2575
01:25:15,440 --> 01:25:17,440
this makes it easier for stakeholders to
2576
01:25:17,440 --> 01:25:18,639
draw conclusions
2577
01:25:18,639 --> 01:25:20,400
make decisions and come up with
2578
01:25:20,400 --> 01:25:21,760
strategies
2579
01:25:21,760 --> 01:25:24,159
some popular visualization tools are
2580
01:25:24,159 --> 01:25:24,880
tableau
2581
01:25:24,880 --> 01:25:27,760
and looker data analysts like using
2582
01:25:27,760 --> 01:25:28,880
tableau because
2583
01:25:28,880 --> 01:25:30,800
it helps them create visuals that are
2584
01:25:30,800 --> 01:25:32,400
very easy to understand
2585
01:25:32,400 --> 01:25:34,800
this means that even non-technical users
2586
01:25:34,800 --> 01:25:37,040
can get the information they need
2587
01:25:37,040 --> 01:25:39,199
looker is also popular with data
2588
01:25:39,199 --> 01:25:40,960
analysts because it gives them
2589
01:25:40,960 --> 01:25:43,679
an easy way to create visuals based on
2590
01:25:43,679 --> 01:25:45,440
the results of a query
2591
01:25:45,440 --> 01:25:47,679
with looker you can give stakeholders a
2592
01:25:47,679 --> 01:25:48,960
complete picture
2593
01:25:48,960 --> 01:25:50,560
of your work by showing them
2594
01:25:50,560 --> 01:25:52,080
visualization data
2595
01:25:52,080 --> 01:25:54,719
and the actual data related to it all
2596
01:25:54,719 --> 01:25:55,679
visualization
2597
01:25:55,679 --> 01:25:58,000
tools have great features that are
2598
01:25:58,000 --> 01:26:00,320
useful in different situations
2599
01:26:00,320 --> 01:26:02,719
soon you'll learn how to decide which
2600
01:26:02,719 --> 01:26:05,760
tool to use for a particular job
2601
01:26:05,760 --> 01:26:07,360
and that's everything you need to know
2602
01:26:07,360 --> 01:26:09,040
about the data life cycle
2603
01:26:09,040 --> 01:26:11,760
and the data analysis process you'll get
2604
01:26:11,760 --> 01:26:13,120
a chance to test out
2605
01:26:13,120 --> 01:26:15,440
what you know so you can feel confident
2606
01:26:15,440 --> 01:26:16,400
moving forward
2607
01:26:16,400 --> 01:26:19,040
in this course feel free to take some
2608
01:26:19,040 --> 01:26:21,199
time to re-familiarize yourself with the
2609
01:26:21,199 --> 01:26:22,320
concepts
2610
01:26:22,320 --> 01:26:24,159
and when you're ready give it your best
2611
01:26:24,159 --> 01:26:26,560
shot if you're ever unsure of an answer
2612
01:26:26,560 --> 01:26:28,400
you can always go back and review the
2613
01:26:28,400 --> 01:26:30,000
videos and readings
2614
01:26:30,000 --> 01:26:31,520
then you'll be ready to move on to the
2615
01:26:31,520 --> 01:26:33,440
next set of videos where we'll continue
2616
01:26:33,440 --> 01:26:35,199
exploring the data analytics tools
2617
01:26:35,199 --> 01:26:36,000
you've already
2618
01:26:36,000 --> 01:26:38,159
covered and you'll get some really
2619
01:26:38,159 --> 01:26:39,360
fascinating insights
2620
01:26:39,360 --> 01:26:42,320
into exactly how they work before long
2621
01:26:42,320 --> 01:26:44,080
you'll have the knowledge and confidence
2622
01:26:44,080 --> 01:26:48,400
to start using them yourself stay tuned
2623
01:26:48,400 --> 01:26:51,120
welcome back in the next few videos
2624
01:26:51,120 --> 01:26:52,880
you'll continue to explore the data
2625
01:26:52,880 --> 01:26:54,000
analytics tools
2626
01:26:54,000 --> 01:26:56,400
we discussed earlier and you'll get the
2627
01:26:56,400 --> 01:26:57,600
chance to see them
2628
01:26:57,600 --> 01:26:59,920
in action a little bit this will give
2629
01:26:59,920 --> 01:27:01,840
you a clearer picture of how to use
2630
01:27:01,840 --> 01:27:03,040
these tools
2631
01:27:03,040 --> 01:27:05,199
the rest of the program will build on
2632
01:27:05,199 --> 01:27:07,120
from what you learn here
2633
01:27:07,120 --> 01:27:08,719
we'll start with a closer look at
2634
01:27:08,719 --> 01:27:10,159
spreadsheets
2635
01:27:10,159 --> 01:27:12,239
we'll break spreadsheets down to their
2636
01:27:12,239 --> 01:27:14,159
basics to better understand
2637
01:27:14,159 --> 01:27:16,560
a few of their features and functions
2638
01:27:16,560 --> 01:27:18,400
you'll also learn how you might want to
2639
01:27:18,400 --> 01:27:19,199
use them
2640
01:27:19,199 --> 01:27:21,600
in your work as a data analyst for
2641
01:27:21,600 --> 01:27:22,639
example
2642
01:27:22,639 --> 01:27:24,560
how do you sort your data to make it
2643
01:27:24,560 --> 01:27:26,000
easier to use
2644
01:27:26,000 --> 01:27:29,840
we'll find out next we'll see sql in
2645
01:27:29,840 --> 01:27:31,120
action
2646
01:27:31,120 --> 01:27:33,840
data analysts use sql in their work all
2647
01:27:33,840 --> 01:27:34,800
the time
2648
01:27:34,800 --> 01:27:36,400
like when they need a large amount of
2649
01:27:36,400 --> 01:27:37,920
data in seconds
2650
01:27:37,920 --> 01:27:40,560
to help answer a quick business question
2651
01:27:40,560 --> 01:27:41,840
chances are
2652
01:27:41,840 --> 01:27:45,760
you're not familiar with sql that's okay
2653
01:27:45,760 --> 01:27:48,080
you'll learn how using sql is just like
2654
01:27:48,080 --> 01:27:49,120
ordering food
2655
01:27:49,120 --> 01:27:52,000
at a super speedy restaurant your sql
2656
01:27:52,000 --> 01:27:52,800
query
2657
01:27:52,800 --> 01:27:55,199
might not be as delicious but you won't
2658
01:27:55,199 --> 01:27:56,080
have to wait long
2659
01:27:56,080 --> 01:27:59,440
to get your order speaking of food
2660
01:27:59,440 --> 01:28:01,920
what better topic than dessert you can
2661
01:28:01,920 --> 01:28:03,600
think of data visualization
2662
01:28:03,600 --> 01:28:06,080
as the dessert to the meal of data
2663
01:28:06,080 --> 01:28:07,280
analytics
2664
01:28:07,280 --> 01:28:09,679
it's served at the end of your analysis
2665
01:28:09,679 --> 01:28:11,520
after you've done what you need to get
2666
01:28:11,520 --> 01:28:12,639
the right data
2667
01:28:12,639 --> 01:28:15,920
for a question or task we've already
2668
01:28:15,920 --> 01:28:17,600
seen that visualizations
2669
01:28:17,600 --> 01:28:20,239
come in a lot of forms like graphs or
2670
01:28:20,239 --> 01:28:21,199
charts
2671
01:28:21,199 --> 01:28:23,600
and just like dessert they're a treat to
2672
01:28:23,600 --> 01:28:24,960
look at
2673
01:28:24,960 --> 01:28:26,480
you'll learn more about these visual
2674
01:28:26,480 --> 01:28:28,960
representations and see other examples
2675
01:28:28,960 --> 01:28:31,679
of how they might look then you'll get
2676
01:28:31,679 --> 01:28:33,360
to talk about visualizations
2677
01:28:33,360 --> 01:28:35,360
with other future data analysts just
2678
01:28:35,360 --> 01:28:36,800
like yourself
2679
01:28:36,800 --> 01:28:39,360
we'll wrap things up with an assessment
2680
01:28:39,360 --> 01:28:41,040
but you'll have time to review
2681
01:28:41,040 --> 01:28:44,080
what you've learned before then okay
2682
01:28:44,080 --> 01:28:47,440
let's keep going by the way is anyone
2683
01:28:47,440 --> 01:28:50,000
else hungry now
2684
01:28:50,000 --> 01:28:53,920
on october 17 2019 we celebrated the
2685
01:28:53,920 --> 01:28:56,000
40th anniversary of a very special
2686
01:28:56,000 --> 01:28:58,719
event well special for people like me
2687
01:28:58,719 --> 01:28:59,760
anyway
2688
01:28:59,760 --> 01:29:03,920
in 1979 visit calc was introduced to the
2689
01:29:03,920 --> 01:29:04,480
world
2690
01:29:04,480 --> 01:29:06,159
as the first computer spreadsheet
2691
01:29:06,159 --> 01:29:08,800
program while spreadsheets have changed
2692
01:29:08,800 --> 01:29:10,239
a lot since then
2693
01:29:10,239 --> 01:29:12,880
it was still an important achievement
2694
01:29:12,880 --> 01:29:13,840
and so now
2695
01:29:13,840 --> 01:29:16,800
we celebrate october 17th every year as
2696
01:29:16,800 --> 01:29:18,639
spreadsheet day
2697
01:29:18,639 --> 01:29:20,960
while there's a good chance you've never
2698
01:29:20,960 --> 01:29:23,199
been to a spreadsheet day party
2699
01:29:23,199 --> 01:29:25,280
spreadsheets are a big part of data
2700
01:29:25,280 --> 01:29:26,400
analytics
2701
01:29:26,400 --> 01:29:27,760
the sooner you make friends with
2702
01:29:27,760 --> 01:29:29,760
spreadsheets the better
2703
01:29:29,760 --> 01:29:31,760
trust me they'll save you a lot of time
2704
01:29:31,760 --> 01:29:33,040
as a data analyst
2705
01:29:33,040 --> 01:29:35,520
and make your job easier this
2706
01:29:35,520 --> 01:29:36,400
spreadsheet
2707
01:29:36,400 --> 01:29:38,560
is one example of how an organized
2708
01:29:38,560 --> 01:29:39,920
spreadsheet might look
2709
01:29:39,920 --> 01:29:41,840
in this video we'll demonstrate some
2710
01:29:41,840 --> 01:29:43,600
basic spreadsheet concepts
2711
01:29:43,600 --> 01:29:46,480
for all of you who are new to this world
2712
01:29:46,480 --> 01:29:48,239
this might be a review for some of you
2713
01:29:48,239 --> 01:29:50,159
more experienced folks out there
2714
01:29:50,159 --> 01:29:52,159
but it never hurts to practice what you
2715
01:29:52,159 --> 01:29:53,280
know
2716
01:29:53,280 --> 01:29:55,520
plus you might still learn a new trick
2717
01:29:55,520 --> 01:29:56,400
or two
2718
01:29:56,400 --> 01:29:58,560
i showed you this image earlier let's
2719
01:29:58,560 --> 01:29:59,760
explore it further
2720
01:29:59,760 --> 01:30:01,520
because it's a great example of the
2721
01:30:01,520 --> 01:30:04,159
three main features of a spreadsheet
2722
01:30:04,159 --> 01:30:07,440
cells rows and columns they'll be a part
2723
01:30:07,440 --> 01:30:08,000
of almost
2724
01:30:08,000 --> 01:30:09,920
everything you do in a spreadsheet from
2725
01:30:09,920 --> 01:30:11,600
making a simple grocery list
2726
01:30:11,600 --> 01:30:14,560
to analyzing a complex data set i use
2727
01:30:14,560 --> 01:30:16,320
spreadsheets to manage everything from
2728
01:30:16,320 --> 01:30:18,159
my own personal finances
2729
01:30:18,159 --> 01:30:19,920
to the annual homecoming party my
2730
01:30:19,920 --> 01:30:22,239
friends and i have every year
2731
01:30:22,239 --> 01:30:24,400
i'm the planner so i use a spreadsheet
2732
01:30:24,400 --> 01:30:26,080
to keep things in order
2733
01:30:26,080 --> 01:30:28,639
making sure we have everything we need
2734
01:30:28,639 --> 01:30:30,560
speaking of keeping things in order
2735
01:30:30,560 --> 01:30:32,639
the columns in a spreadsheet are ordered
2736
01:30:32,639 --> 01:30:33,600
by letter
2737
01:30:33,600 --> 01:30:36,239
and the rows are ordered by number so
2738
01:30:36,239 --> 01:30:38,480
when you talk about a specific cell
2739
01:30:38,480 --> 01:30:40,880
you name it by combining the column
2740
01:30:40,880 --> 01:30:41,520
letter
2741
01:30:41,520 --> 01:30:44,239
and row number where the cell is located
2742
01:30:44,239 --> 01:30:45,199
for example
2743
01:30:45,199 --> 01:30:48,159
in this spreadsheet the word row is in
2744
01:30:48,159 --> 01:30:49,760
cell d3
2745
01:30:49,760 --> 01:30:52,880
pretty simple right let's get started in
2746
01:30:52,880 --> 01:30:54,400
an actual spreadsheet
2747
01:30:54,400 --> 01:30:56,480
you can complete all of these steps in
2748
01:30:56,480 --> 01:30:58,880
just about any spreadsheet program
2749
01:30:58,880 --> 01:31:00,560
let's get to know your spreadsheet a
2750
01:31:00,560 --> 01:31:02,560
little better now
2751
01:31:02,560 --> 01:31:04,719
alright we'll start with some basic
2752
01:31:04,719 --> 01:31:06,159
operations
2753
01:31:06,159 --> 01:31:08,719
keep in mind as an analyst you won't
2754
01:31:08,719 --> 01:31:10,639
always create your own data sets
2755
01:31:10,639 --> 01:31:13,679
but for now let's do just that i'll
2756
01:31:13,679 --> 01:31:14,560
click in cell
2757
01:31:14,560 --> 01:31:17,679
a2 and type my first name in the cell
2758
01:31:17,679 --> 01:31:21,360
like this next i'll click in cell
2759
01:31:21,360 --> 01:31:24,400
b2 and type my last name don't worry if
2760
01:31:24,400 --> 01:31:26,400
your name doesn't fit in the cell
2761
01:31:26,400 --> 01:31:28,719
you can always make the columns wider if
2762
01:31:28,719 --> 01:31:30,080
you need to
2763
01:31:30,080 --> 01:31:32,800
all you have to do is click and drag the
2764
01:31:32,800 --> 01:31:34,320
right edge of the column
2765
01:31:34,320 --> 01:31:37,120
until the name fits or you can also use
2766
01:31:37,120 --> 01:31:38,639
the text wrapping feature
2767
01:31:38,639 --> 01:31:41,120
which will set cells to automatically
2768
01:31:41,120 --> 01:31:42,239
change their height
2769
01:31:42,239 --> 01:31:45,360
and allow the text in the cell to fit to
2770
01:31:45,360 --> 01:31:46,480
use this feature
2771
01:31:46,480 --> 01:31:49,679
select the cells columns or rows with
2772
01:31:49,679 --> 01:31:51,360
text
2773
01:31:51,360 --> 01:31:54,159
then use the format menu to look at the
2774
01:31:54,159 --> 01:31:55,920
text wrapping options
2775
01:31:55,920 --> 01:31:58,480
it is automatically set to allow the
2776
01:31:58,480 --> 01:31:59,120
text to
2777
01:31:59,120 --> 01:32:02,480
overflow out of the cell but you can
2778
01:32:02,480 --> 01:32:04,320
wrap the text instead
2779
01:32:04,320 --> 01:32:07,360
so all of the text is visible the clip
2780
01:32:07,360 --> 01:32:07,920
option
2781
01:32:07,920 --> 01:32:10,480
will cut off the text in the cell so
2782
01:32:10,480 --> 01:32:11,440
only the text
2783
01:32:11,440 --> 01:32:14,800
that fits is visible there it is
2784
01:32:14,800 --> 01:32:17,360
we've added data now let's label the
2785
01:32:17,360 --> 01:32:18,159
data
2786
01:32:18,159 --> 01:32:20,400
this is important for organization
2787
01:32:20,400 --> 01:32:22,719
adding labels to the top of the columns
2788
01:32:22,719 --> 01:32:24,719
will make it easier to reference and
2789
01:32:24,719 --> 01:32:26,159
find data later on
2790
01:32:26,159 --> 01:32:29,040
when you're doing analysis the column
2791
01:32:29,040 --> 01:32:29,600
labels
2792
01:32:29,600 --> 01:32:32,159
are usually called attributes an
2793
01:32:32,159 --> 01:32:34,560
attribute is a characteristic or quality
2794
01:32:34,560 --> 01:32:37,120
of data used to label a column in a
2795
01:32:37,120 --> 01:32:38,320
table
2796
01:32:38,320 --> 01:32:40,800
you might hear them called variables or
2797
01:32:40,800 --> 01:32:42,800
a few other names too
2798
01:32:42,800 --> 01:32:45,120
all right let's add some attributes to
2799
01:32:45,120 --> 01:32:46,239
our data
2800
01:32:46,239 --> 01:32:49,679
i'll click in cell a1 and type the words
2801
01:32:49,679 --> 01:32:54,080
first name
2802
01:32:54,080 --> 01:32:58,320
in cell b1 i'll type last name
2803
01:32:58,320 --> 01:33:00,480
we'll make these attributes bold so they
2804
01:33:00,480 --> 01:33:01,920
stand out more
2805
01:33:01,920 --> 01:33:04,400
spreadsheets can be really big so you
2806
01:33:04,400 --> 01:33:05,920
want to make sure your data is clearly
2807
01:33:05,920 --> 01:33:08,480
labeled and easy to find
2808
01:33:08,480 --> 01:33:10,719
so let's make these attributes stand out
2809
01:33:10,719 --> 01:33:12,000
i can use my cursor
2810
01:33:12,000 --> 01:33:14,560
to select the cells with the attributes
2811
01:33:14,560 --> 01:33:15,120
then
2812
01:33:15,120 --> 01:33:17,040
i'll click the bold icon to make the
2813
01:33:17,040 --> 01:33:18,400
attributes bold
2814
01:33:18,400 --> 01:33:21,120
looking good so far ready to add some
2815
01:33:21,120 --> 01:33:22,560
more data
2816
01:33:22,560 --> 01:33:24,800
let's start with some new attributes
2817
01:33:24,800 --> 01:33:26,400
first i'll add a column
2818
01:33:26,400 --> 01:33:29,760
for age by typing age in cell
2819
01:33:29,760 --> 01:33:33,280
c1 then i'll add two more attributes
2820
01:33:33,280 --> 01:33:36,239
in the next two columns let's go with
2821
01:33:36,239 --> 01:33:37,199
favorite color
2822
01:33:37,199 --> 01:33:40,960
and favorite dessert i'll make them bold
2823
01:33:40,960 --> 01:33:42,320
too
2824
01:33:42,320 --> 01:33:44,400
and to fit the labels in the cells i'll
2825
01:33:44,400 --> 01:33:46,159
adjust the size of the columns
2826
01:33:46,159 --> 01:33:48,800
just like before now keep in mind there
2827
01:33:48,800 --> 01:33:50,000
are more ways to adjust
2828
01:33:50,000 --> 01:33:52,560
the size of columns and rows if you have
2829
01:33:52,560 --> 01:33:54,400
questions about using spreadsheets
2830
01:33:54,400 --> 01:33:56,480
a quick search online will usually help
2831
01:33:56,480 --> 01:33:58,159
you find what you need
2832
01:33:58,159 --> 01:34:00,239
we've also included a reading with more
2833
01:34:00,239 --> 01:34:01,360
tips and information
2834
01:34:01,360 --> 01:34:04,480
about spreadsheets okay
2835
01:34:04,480 --> 01:34:07,360
let's get back to it now i can add my
2836
01:34:07,360 --> 01:34:09,360
own data to the data set
2837
01:34:09,360 --> 01:34:12,320
i'll type in my age and favorite color
2838
01:34:12,320 --> 01:34:13,280
and dessert
2839
01:34:13,280 --> 01:34:17,270
in the appropriate cells
2840
01:34:17,280 --> 01:34:28,310
next i'll add data for two more people
2841
01:34:28,320 --> 01:34:31,360
we now have three rows of data in a data
2842
01:34:31,360 --> 01:34:32,960
set a row is called
2843
01:34:32,960 --> 01:34:36,000
an observation an observation includes
2844
01:34:36,000 --> 01:34:37,840
all of the attributes for something
2845
01:34:37,840 --> 01:34:40,639
contained in a row of a data table
2846
01:34:40,639 --> 01:34:43,679
in this case row 3 is an observation
2847
01:34:43,679 --> 01:34:46,800
of willa stein because we see all of her
2848
01:34:46,800 --> 01:34:49,440
attributes in this row
2849
01:34:49,440 --> 01:34:51,280
so now we know spreadsheets let you do
2850
01:34:51,280 --> 01:34:52,960
lots of things with data
2851
01:34:52,960 --> 01:34:54,960
you can store and organize data like
2852
01:34:54,960 --> 01:34:56,639
we've done in this spreadsheet
2853
01:34:56,639 --> 01:34:58,719
but you can go even further and
2854
01:34:58,719 --> 01:35:00,000
reorganize existing
2855
01:35:00,000 --> 01:35:02,960
data too here i'll show you how let's
2856
01:35:02,960 --> 01:35:03,840
say we want to
2857
01:35:03,840 --> 01:35:06,159
organize our data by age there's a
2858
01:35:06,159 --> 01:35:08,000
simple way to do that
2859
01:35:08,000 --> 01:35:10,320
first we'll need to select all of our
2860
01:35:10,320 --> 01:35:11,840
columns with data
2861
01:35:11,840 --> 01:35:14,239
so that all of it gets reorganized
2862
01:35:14,239 --> 01:35:15,360
together
2863
01:35:15,360 --> 01:35:18,080
then we can go to our data menu here we
2864
01:35:18,080 --> 01:35:19,520
have some options
2865
01:35:19,520 --> 01:35:22,639
let's select sort range this will let us
2866
01:35:22,639 --> 01:35:23,119
choose
2867
01:35:23,119 --> 01:35:26,400
how to organize the column next we'll
2868
01:35:26,400 --> 01:35:26,960
choose
2869
01:35:26,960 --> 01:35:30,159
a to z which will organize our numbers
2870
01:35:30,159 --> 01:35:32,320
in order from the smallest to the
2871
01:35:32,320 --> 01:35:33,440
largest
2872
01:35:33,440 --> 01:35:35,840
now we want to watch out for our header
2873
01:35:35,840 --> 01:35:36,560
row
2874
01:35:36,560 --> 01:35:39,600
which is the word age the attribute
2875
01:35:39,600 --> 01:35:42,800
for this column we'll check that box
2876
01:35:42,800 --> 01:35:45,679
this makes sure that the word age stays
2877
01:35:45,679 --> 01:35:46,960
in place
2878
01:35:46,960 --> 01:35:50,719
all right now we're ready to sort
2879
01:35:50,719 --> 01:35:53,760
voila we just reorganized our data by
2880
01:35:53,760 --> 01:35:54,480
sorting it
2881
01:35:54,480 --> 01:35:57,600
from the smallest number to largest and
2882
01:35:57,600 --> 01:35:58,880
as we go further
2883
01:35:58,880 --> 01:36:00,719
you'll discover lots of other ways to
2884
01:36:00,719 --> 01:36:02,639
work with data in a spreadsheet
2885
01:36:02,639 --> 01:36:05,679
including functions and formulas
2886
01:36:05,679 --> 01:36:07,760
let's finish with a quick example of a
2887
01:36:07,760 --> 01:36:08,800
formula
2888
01:36:08,800 --> 01:36:10,960
you can think of formulas as one way of
2889
01:36:10,960 --> 01:36:13,360
manipulating data in a spreadsheet
2890
01:36:13,360 --> 01:36:15,679
formulas are like a calculator but more
2891
01:36:15,679 --> 01:36:16,800
powerful
2892
01:36:16,800 --> 01:36:19,119
a formula is a set of instructions that
2893
01:36:19,119 --> 01:36:21,280
performs a specific calculation
2894
01:36:21,280 --> 01:36:23,920
using the data in a spreadsheet to do
2895
01:36:23,920 --> 01:36:24,400
this
2896
01:36:24,400 --> 01:36:26,800
the formula uses cell references for the
2897
01:36:26,800 --> 01:36:28,560
values it's calculating
2898
01:36:28,560 --> 01:36:31,199
let me show you here we go we'll click
2899
01:36:31,199 --> 01:36:32,480
in the next cell
2900
01:36:32,480 --> 01:36:35,679
in the age column then we'll type an
2901
01:36:35,679 --> 01:36:38,960
equal sign all formulas begin
2902
01:36:38,960 --> 01:36:42,800
with this symbol next we type average
2903
01:36:42,800 --> 01:36:45,199
this is the function we are using in the
2904
01:36:45,199 --> 01:36:46,320
formula
2905
01:36:46,320 --> 01:36:48,400
we've briefly discussed how functions
2906
01:36:48,400 --> 01:36:49,600
work before
2907
01:36:49,600 --> 01:36:51,360
but it's okay if you don't completely
2908
01:36:51,360 --> 01:36:53,040
understand them yet
2909
01:36:53,040 --> 01:36:55,920
we'll take a closer look later on in
2910
01:36:55,920 --> 01:36:57,040
this case
2911
01:36:57,040 --> 01:36:59,760
we follow the function average with the
2912
01:36:59,760 --> 01:37:00,239
left
2913
01:37:00,239 --> 01:37:03,920
parentheses now we can add the names of
2914
01:37:03,920 --> 01:37:04,800
the cells
2915
01:37:04,800 --> 01:37:07,679
where we find the data we're using these
2916
01:37:07,679 --> 01:37:09,199
are the cell references
2917
01:37:09,199 --> 01:37:11,600
the formula will use to make its
2918
01:37:11,600 --> 01:37:13,199
calculation
2919
01:37:13,199 --> 01:37:16,239
we'll start at the top with cell c2
2920
01:37:16,239 --> 01:37:19,119
c2 represents the value in the cell in
2921
01:37:19,119 --> 01:37:20,800
this case 36
2922
01:37:20,800 --> 01:37:23,360
then we'll add a colon next to it which
2923
01:37:23,360 --> 01:37:23,920
shows
2924
01:37:23,920 --> 01:37:26,080
that we have a range of numbers in
2925
01:37:26,080 --> 01:37:27,440
consecutive cells
2926
01:37:27,440 --> 01:37:30,239
finally we complete our formula by
2927
01:37:30,239 --> 01:37:32,159
adding the last cell reference in the
2928
01:37:32,159 --> 01:37:33,119
range
2929
01:37:33,119 --> 01:37:37,119
c4 and a right parenthesis to end it
2930
01:37:37,119 --> 01:37:39,440
then we press enter to perform the
2931
01:37:39,440 --> 01:37:40,560
calculation
2932
01:37:40,560 --> 01:37:43,040
and there it is the formula has given us
2933
01:37:43,040 --> 01:37:44,400
the average age
2934
01:37:44,400 --> 01:37:47,520
of the ages in this data set we've just
2935
01:37:47,520 --> 01:37:50,719
analyzed some data we'll want to store
2936
01:37:50,719 --> 01:37:52,400
the data for later use
2937
01:37:52,400 --> 01:37:54,639
in google sheets a spreadsheet is
2938
01:37:54,639 --> 01:37:55,760
automatically saved
2939
01:37:55,760 --> 01:37:58,639
in your google drive for excel and other
2940
01:37:58,639 --> 01:37:59,520
spreadsheets
2941
01:37:59,520 --> 01:38:02,800
you'll save them as a file and now you
2942
01:38:02,800 --> 01:38:03,119
know
2943
01:38:03,119 --> 01:38:06,320
some basics for using spreadsheets once
2944
01:38:06,320 --> 01:38:08,000
you're used to these concepts
2945
01:38:08,000 --> 01:38:10,480
you'll be able to learn even more about
2946
01:38:10,480 --> 01:38:12,000
spreadsheet tools
2947
01:38:12,000 --> 01:38:14,320
it's a lot to digest so feel free to
2948
01:38:14,320 --> 01:38:16,800
re-watch and practice on your own
2949
01:38:16,800 --> 01:38:18,480
you can even make your own version of
2950
01:38:18,480 --> 01:38:21,040
this spreadsheet with your own data
2951
01:38:21,040 --> 01:38:22,719
we'll work together in a spreadsheet
2952
01:38:22,719 --> 01:38:24,239
soon as well
2953
01:38:24,239 --> 01:38:26,800
for now good job for sticking with me
2954
01:38:26,800 --> 01:38:27,679
through this
2955
01:38:27,679 --> 01:38:31,840
it'll be worth it as you might remember
2956
01:38:31,840 --> 01:38:34,719
earlier we touched on the query language
2957
01:38:34,719 --> 01:38:35,920
sql
2958
01:38:35,920 --> 01:38:39,600
in this video you'll see sql in action
2959
01:38:39,600 --> 01:38:42,080
and finally learn what you can do with
2960
01:38:42,080 --> 01:38:43,760
it by taking a look
2961
01:38:43,760 --> 01:38:47,119
at some examples of specific queries
2962
01:38:47,119 --> 01:38:51,600
i guess you can call this the sql sql
2963
01:38:51,600 --> 01:38:53,600
we'll try to make this one at least as
2964
01:38:53,600 --> 01:38:55,280
good as the first
2965
01:38:55,280 --> 01:38:58,159
remember sql can do lots of the same
2966
01:38:58,159 --> 01:38:59,119
things with data
2967
01:38:59,119 --> 01:39:01,840
that spreadsheets can you can use it to
2968
01:39:01,840 --> 01:39:02,880
store
2969
01:39:02,880 --> 01:39:05,440
organize and analyze your data among
2970
01:39:05,440 --> 01:39:06,800
other things
2971
01:39:06,800 --> 01:39:10,080
but like any good sql it is on a larger
2972
01:39:10,080 --> 01:39:11,199
scale
2973
01:39:11,199 --> 01:39:14,400
bigger more action-packed think of it as
2974
01:39:14,400 --> 01:39:16,639
super-sized spreadsheets
2975
01:39:16,639 --> 01:39:19,040
for example you might want to consider a
2976
01:39:19,040 --> 01:39:19,920
spreadsheet
2977
01:39:19,920 --> 01:39:22,560
when you have a smaller data set like
2978
01:39:22,560 --> 01:39:24,080
100 rows
2979
01:39:24,080 --> 01:39:26,880
but if your data seems to go on forever
2980
01:39:26,880 --> 01:39:28,000
and your spreadsheet
2981
01:39:28,000 --> 01:39:30,880
is struggling to keep up sql would be
2982
01:39:30,880 --> 01:39:32,560
the way to go
2983
01:39:32,560 --> 01:39:35,119
when you use sql you'll need a place
2984
01:39:35,119 --> 01:39:36,639
where the sql language
2985
01:39:36,639 --> 01:39:39,600
is understood if you've ever gone
2986
01:39:39,600 --> 01:39:40,320
somewhere
2987
01:39:40,320 --> 01:39:42,560
and not known the language it can be
2988
01:39:42,560 --> 01:39:44,719
challenging to communicate
2989
01:39:44,719 --> 01:39:46,400
you might think you're asking for one
2990
01:39:46,400 --> 01:39:48,080
thing and get something completely
2991
01:39:48,080 --> 01:39:49,040
different
2992
01:39:49,040 --> 01:39:52,080
well sql knows that feeling
2993
01:39:52,080 --> 01:39:53,920
sql needs a database that will
2994
01:39:53,920 --> 01:39:55,199
understand its
2995
01:39:55,199 --> 01:39:58,560
language so let's talk there are a
2996
01:39:58,560 --> 01:40:00,560
number of databases out there
2997
01:40:00,560 --> 01:40:04,560
that use sql you may use several of them
2998
01:40:04,560 --> 01:40:07,280
during your time as a data analyst but
2999
01:40:07,280 --> 01:40:08,560
here's the thing
3000
01:40:08,560 --> 01:40:11,199
no matter which database you use sql
3001
01:40:11,199 --> 01:40:13,520
basically works the same in each
3002
01:40:13,520 --> 01:40:17,760
for example in sql queries are universal
3003
01:40:17,760 --> 01:40:20,080
we've talked about queries before but it
3004
01:40:20,080 --> 01:40:22,480
never hurts to have a refresher
3005
01:40:22,480 --> 01:40:25,119
a query is the way we use sql to
3006
01:40:25,119 --> 01:40:27,600
communicate with the database
3007
01:40:27,600 --> 01:40:30,480
here's the structure of a basic query
3008
01:40:30,480 --> 01:40:32,800
you can see that with this query
3009
01:40:32,800 --> 01:40:36,639
we can select specific data from a table
3010
01:40:36,639 --> 01:40:39,679
by adding where we can filter the data
3011
01:40:39,679 --> 01:40:43,199
based on certain conditions all right
3012
01:40:43,199 --> 01:40:45,040
let's get started we'll open our
3013
01:40:45,040 --> 01:40:47,440
database and see how sql can communicate
3014
01:40:47,440 --> 01:40:48,000
with it
3015
01:40:48,000 --> 01:40:51,440
to do some simple data tasks
3016
01:40:51,440 --> 01:40:54,639
first let's select our data we'll use an
3017
01:40:54,639 --> 01:40:55,440
asterisk
3018
01:40:55,440 --> 01:40:58,320
to select all of the data from the table
3019
01:40:58,320 --> 01:41:00,000
and with that simple query
3020
01:41:00,000 --> 01:41:03,199
the database calls up the table we need
3021
01:41:03,199 --> 01:41:06,080
magic let's add where to the query to
3022
01:41:06,080 --> 01:41:07,360
show how that changes
3023
01:41:07,360 --> 01:41:13,910
with data we get
3024
01:41:13,920 --> 01:41:16,560
you can see that the data now only shows
3025
01:41:16,560 --> 01:41:17,440
movies
3026
01:41:17,440 --> 01:41:20,719
that are in the action genre and that's
3027
01:41:20,719 --> 01:41:21,360
it
3028
01:41:21,360 --> 01:41:24,560
a basic query in sql pretty cool huh
3029
01:41:24,560 --> 01:41:26,400
there are plenty of other commands that
3030
01:41:26,400 --> 01:41:27,920
you'll use in queries
3031
01:41:27,920 --> 01:41:30,880
as you continue for now though we can
3032
01:41:30,880 --> 01:41:32,239
celebrate learning about
3033
01:41:32,239 --> 01:41:35,360
three big ones select from
3034
01:41:35,360 --> 01:41:38,880
and where as you continue the program
3035
01:41:38,880 --> 01:41:41,119
you'll have the opportunity to use sql
3036
01:41:41,119 --> 01:41:42,080
yourself
3037
01:41:42,080 --> 01:41:44,560
so i hope that this video was a useful
3038
01:41:44,560 --> 01:41:45,360
sneak peek
3039
01:41:45,360 --> 01:41:48,400
at what's coming later like with any new
3040
01:41:48,400 --> 01:41:49,280
language
3041
01:41:49,280 --> 01:41:52,400
learning it takes time and now it's time
3042
01:41:52,400 --> 01:41:58,870
to move on
3043
01:41:58,880 --> 01:42:01,280
i'm angie i'm a program manager of
3044
01:42:01,280 --> 01:42:02,800
engineering at google
3045
01:42:02,800 --> 01:42:05,040
i'm currently working on the data
3046
01:42:05,040 --> 01:42:06,560
analytics certificate
3047
01:42:06,560 --> 01:42:08,639
and previously i was a researcher in
3048
01:42:08,639 --> 01:42:10,480
people analytics i was also
3049
01:42:10,480 --> 01:42:12,400
what i call an analytical mercenary
3050
01:42:12,400 --> 01:42:14,000
working for a lot of different companies
3051
01:42:14,000 --> 01:42:16,080
to help them make sense of their data
3052
01:42:16,080 --> 01:42:17,280
every time i learn
3053
01:42:17,280 --> 01:42:20,159
a new skill i feel like i'm learning how
3054
01:42:20,159 --> 01:42:20,880
to speak
3055
01:42:20,880 --> 01:42:23,840
all over again i remember the first time
3056
01:42:23,840 --> 01:42:25,920
i learned sql i was so frustrated
3057
01:42:25,920 --> 01:42:26,320
because
3058
01:42:26,320 --> 01:42:28,639
everyone around me just it felt like
3059
01:42:28,639 --> 01:42:29,520
they were fluent
3060
01:42:29,520 --> 01:42:31,679
they knew exactly what they were doing
3061
01:42:31,679 --> 01:42:32,719
and i remember
3062
01:42:32,719 --> 01:42:34,480
struggling with the most basic things
3063
01:42:34,480 --> 01:42:36,239
just like getting the data
3064
01:42:36,239 --> 01:42:38,800
out of the table right or i remember
3065
01:42:38,800 --> 01:42:40,800
somebody asked me just to find like an
3066
01:42:40,800 --> 01:42:42,560
average of something and i kept on
3067
01:42:42,560 --> 01:42:44,560
getting an error and it really does feel
3068
01:42:44,560 --> 01:42:46,400
like you're learning a new language
3069
01:42:46,400 --> 01:42:48,719
and you're at toddler level and everyone
3070
01:42:48,719 --> 01:42:50,719
around you is like maybe fluent
3071
01:42:50,719 --> 01:42:52,400
so my parents immigrated to this country
3072
01:42:52,400 --> 01:42:54,560
when they were in their 30s so after
3073
01:42:54,560 --> 01:42:56,239
you know they had learned another
3074
01:42:56,239 --> 01:42:58,000
language and they had to start over and
3075
01:42:58,000 --> 01:42:58,400
learn
3076
01:42:58,400 --> 01:43:02,080
you know english and i remember as a
3077
01:43:02,080 --> 01:43:03,280
child watching them
3078
01:43:03,280 --> 01:43:05,520
struggle every day to pick up a new
3079
01:43:05,520 --> 01:43:08,320
language to do really basic things
3080
01:43:08,320 --> 01:43:10,960
like ask for help at the grocery store i
3081
01:43:10,960 --> 01:43:11,520
remember
3082
01:43:11,520 --> 01:43:14,080
calling the cable company when i was six
3083
01:43:14,080 --> 01:43:14,960
asking them
3084
01:43:14,960 --> 01:43:16,320
questions about the bill because my
3085
01:43:16,320 --> 01:43:18,080
parents couldn't and
3086
01:43:18,080 --> 01:43:20,960
i remember how hard they worked to learn
3087
01:43:20,960 --> 01:43:22,320
this new language
3088
01:43:22,320 --> 01:43:25,360
and to become fluent you know and every
3089
01:43:25,360 --> 01:43:27,280
time i'm learning a new data language
3090
01:43:27,280 --> 01:43:29,280
like sql or r i think about how hard
3091
01:43:29,280 --> 01:43:30,639
that must have been
3092
01:43:30,639 --> 01:43:33,679
and i i think if they can do that
3093
01:43:33,679 --> 01:43:35,920
i can learn sql um if they can ask for
3094
01:43:35,920 --> 01:43:37,440
help for the most basic of things
3095
01:43:37,440 --> 01:43:39,440
i can ask the data analyst next to me
3096
01:43:39,440 --> 01:43:41,040
you know how to write a sql statement
3097
01:43:41,040 --> 01:43:43,040
how to get data out of a table
3098
01:43:43,040 --> 01:43:44,560
and that's really helped me is just
3099
01:43:44,560 --> 01:43:46,480
having that mindset and knowing that i
3100
01:43:46,480 --> 01:43:50,790
can ask for help
3101
01:43:50,800 --> 01:43:53,600
wow your data analysis toolbox is
3102
01:43:53,600 --> 01:43:55,040
getting full
3103
01:43:55,040 --> 01:43:57,760
learning about both spreadsheets and sql
3104
01:43:57,760 --> 01:43:58,480
will get you
3105
01:43:58,480 --> 01:44:01,119
far in the world of data analysis
3106
01:44:01,119 --> 01:44:02,880
there's more to learn of course
3107
01:44:02,880 --> 01:44:05,040
and lots more tools you'll be able to
3108
01:44:05,040 --> 01:44:07,760
use but your future is looking bright
3109
01:44:07,760 --> 01:44:10,159
and it's about to look even brighter
3110
01:44:10,159 --> 01:44:12,800
because we're here to talk more about
3111
01:44:12,800 --> 01:44:16,000
data visualization
3112
01:44:16,000 --> 01:44:17,840
i'll tell you a little more about the
3113
01:44:17,840 --> 01:44:20,080
role of data visualization tools
3114
01:44:20,080 --> 01:44:22,800
in data analytics and give you a chance
3115
01:44:22,800 --> 01:44:23,280
to see
3116
01:44:23,280 --> 01:44:25,679
those tools in action later in this
3117
01:44:25,679 --> 01:44:27,600
video
3118
01:44:27,600 --> 01:44:29,040
you might remember that data
3119
01:44:29,040 --> 01:44:30,960
visualization is the graphical
3120
01:44:30,960 --> 01:44:33,199
representation of information
3121
01:44:33,199 --> 01:44:35,280
for tons of data analysts it's the most
3122
01:44:35,280 --> 01:44:36,960
exciting part of their job
3123
01:44:36,960 --> 01:44:39,119
because they get to see their hard work
3124
01:44:39,119 --> 01:44:41,760
pay off with something interesting
3125
01:44:41,760 --> 01:44:43,920
not to mention that data visualization
3126
01:44:43,920 --> 01:44:44,800
is beautiful
3127
01:44:44,800 --> 01:44:47,440
and useful i was floored when i got to
3128
01:44:47,440 --> 01:44:48,159
google
3129
01:44:48,159 --> 01:44:50,080
and started to get a quarterly data
3130
01:44:50,080 --> 01:44:51,600
report in my email
3131
01:44:51,600 --> 01:44:53,600
it had a big slide deck where people
3132
01:44:53,600 --> 01:44:54,800
contributed their
3133
01:44:54,800 --> 01:44:57,520
visualizations it was definitely a
3134
01:44:57,520 --> 01:44:58,480
source of light
3135
01:44:58,480 --> 01:45:00,840
as i started to build my own
3136
01:45:00,840 --> 01:45:02,560
visualizations
3137
01:45:02,560 --> 01:45:05,040
if you're not impressed by my story let
3138
01:45:05,040 --> 01:45:06,159
me tell you about
3139
01:45:06,159 --> 01:45:08,800
florence nightingale does that name ring
3140
01:45:08,800 --> 01:45:09,920
a bell
3141
01:45:09,920 --> 01:45:11,840
she's responsible for much of the
3142
01:45:11,840 --> 01:45:14,400
philosophy of modern nursing
3143
01:45:14,400 --> 01:45:16,800
and believe it or not she was also a
3144
01:45:16,800 --> 01:45:18,560
data analyst
3145
01:45:18,560 --> 01:45:22,000
during the crimean war in the 1850s
3146
01:45:22,000 --> 01:45:24,159
thousands of soldiers were dying every
3147
01:45:24,159 --> 01:45:25,280
day
3148
01:45:25,280 --> 01:45:27,040
nightingale wanted to find a way to
3149
01:45:27,040 --> 01:45:29,119
reduce the number of deaths
3150
01:45:29,119 --> 01:45:31,679
after examining the data she found that
3151
01:45:31,679 --> 01:45:33,280
the majority of soldiers
3152
01:45:33,280 --> 01:45:36,159
were dying from preventable conditions
3153
01:45:36,159 --> 01:45:38,159
to convince hospital administrators
3154
01:45:38,159 --> 01:45:39,679
that they needed to focus on these
3155
01:45:39,679 --> 01:45:41,920
conditions she created a chart
3156
01:45:41,920 --> 01:45:43,679
showing the number of deaths over
3157
01:45:43,679 --> 01:45:45,440
several months the much
3158
01:45:45,440 --> 01:45:48,400
larger blue sections individualization
3159
01:45:48,400 --> 01:45:49,199
represent
3160
01:45:49,199 --> 01:45:52,159
the preventable deaths her work directly
3161
01:45:52,159 --> 01:45:52,639
led
3162
01:45:52,639 --> 01:45:55,920
to major changes in patient care and she
3163
01:45:55,920 --> 01:45:57,199
did all of this
3164
01:45:57,199 --> 01:46:02,320
over 150 years ago without a computer
3165
01:46:02,320 --> 01:46:04,239
one of the main reasons nightingale
3166
01:46:04,239 --> 01:46:06,080
created this visualization
3167
01:46:06,080 --> 01:46:08,320
was to make the data easier to digest
3168
01:46:08,320 --> 01:46:09,920
for her audience
3169
01:46:09,920 --> 01:46:11,760
she felt she'd be more successful
3170
01:46:11,760 --> 01:46:13,520
convincing the stakeholders using
3171
01:46:13,520 --> 01:46:14,400
visuals
3172
01:46:14,400 --> 01:46:17,360
instead of just words and numbers she
3173
01:46:17,360 --> 01:46:19,360
was right
3174
01:46:19,360 --> 01:46:22,080
tables filled with data while necessary
3175
01:46:22,080 --> 01:46:23,360
for analysis
3176
01:46:23,360 --> 01:46:25,360
just aren't able to show trends and
3177
01:46:25,360 --> 01:46:26,800
patterns as quickly
3178
01:46:26,800 --> 01:46:30,080
and clearly as visualizations can
3179
01:46:30,080 --> 01:46:31,840
imagine you receive an assignment that
3180
01:46:31,840 --> 01:46:34,159
needs to be completed the same day
3181
01:46:34,159 --> 01:46:37,040
you gather the data you need in a table
3182
01:46:37,040 --> 01:46:38,320
could you explain
3183
01:46:38,320 --> 01:46:41,440
your findings using the table yes you
3184
01:46:41,440 --> 01:46:42,719
probably could
3185
01:46:42,719 --> 01:46:44,800
but a better idea would be to use a
3186
01:46:44,800 --> 01:46:45,920
visualization
3187
01:46:45,920 --> 01:46:49,360
like this bar graph something like this
3188
01:46:49,360 --> 01:46:51,679
makes it much easier for you to explain
3189
01:46:51,679 --> 01:46:52,639
quickly
3190
01:46:52,639 --> 01:46:54,639
and you've got the benefit of a cool
3191
01:46:54,639 --> 01:46:57,679
graphic to back up your analysis
3192
01:46:57,679 --> 01:46:59,840
as a data analyst you'll want to create
3193
01:46:59,840 --> 01:47:00,880
visualizations
3194
01:47:00,880 --> 01:47:03,199
that make the data easy to understand
3195
01:47:03,199 --> 01:47:04,080
and interesting
3196
01:47:04,080 --> 01:47:07,440
to look at so show it off stakeholders
3197
01:47:07,440 --> 01:47:08,000
may not have
3198
01:47:08,000 --> 01:47:11,040
much time to devote to the data your job
3199
01:47:11,040 --> 01:47:12,800
will be to make their time
3200
01:47:12,800 --> 01:47:15,280
worthwhile let's go back to that data
3201
01:47:15,280 --> 01:47:16,400
table we created
3202
01:47:16,400 --> 01:47:19,040
earlier in the course if you created
3203
01:47:19,040 --> 01:47:20,400
your own for practice
3204
01:47:20,400 --> 01:47:23,040
you can open it up now or try this out
3205
01:47:23,040 --> 01:47:23,760
later
3206
01:47:23,760 --> 01:47:26,560
here's the data we added before let's
3207
01:47:26,560 --> 01:47:28,320
create a visualization
3208
01:47:28,320 --> 01:47:31,199
of the data by inserting a chart a bar
3209
01:47:31,199 --> 01:47:32,639
graph
3210
01:47:32,639 --> 01:47:35,440
boom you can see that the spreadsheet
3211
01:47:35,440 --> 01:47:37,520
visualized the data from our table in a
3212
01:47:37,520 --> 01:47:38,000
way
3213
01:47:38,000 --> 01:47:40,400
that made the most sense it created a
3214
01:47:40,400 --> 01:47:41,360
bar graph
3215
01:47:41,360 --> 01:47:43,679
or column chart to compare the ages of
3216
01:47:43,679 --> 01:47:45,520
each person by name
3217
01:47:45,520 --> 01:47:46,960
but you might have figured that out
3218
01:47:46,960 --> 01:47:49,360
already that's the beauty
3219
01:47:49,360 --> 01:47:52,639
of visualization it shows data analysis
3220
01:47:52,639 --> 01:47:56,159
quickly and clearly we can use chart
3221
01:47:56,159 --> 01:47:56,800
editor
3222
01:47:56,800 --> 01:47:58,480
to adjust the chart different
3223
01:47:58,480 --> 01:48:00,080
spreadsheet programs might have
3224
01:48:00,080 --> 01:48:01,840
different ways to do this
3225
01:48:01,840 --> 01:48:03,520
but they all have visualization
3226
01:48:03,520 --> 01:48:05,199
functions and ways
3227
01:48:05,199 --> 01:48:08,159
to edit those visualizations alright for
3228
01:48:08,159 --> 01:48:08,800
now
3229
01:48:08,800 --> 01:48:11,679
let's just look at the suggested charts
3230
01:48:11,679 --> 01:48:14,400
we can make the bars go horizontally
3231
01:48:14,400 --> 01:48:18,159
using a bar chart that looks great
3232
01:48:18,159 --> 01:48:21,750
so let's close the chart editor
3233
01:48:21,760 --> 01:48:24,239
there are lots of options to look at but
3234
01:48:24,239 --> 01:48:26,639
we'll keep it basic for now
3235
01:48:26,639 --> 01:48:29,119
feel free to try other visualizations if
3236
01:48:29,119 --> 01:48:29,840
you practice
3237
01:48:29,840 --> 01:48:32,800
later now we can adjust our chart to
3238
01:48:32,800 --> 01:48:34,320
make our whole spreadsheet
3239
01:48:34,320 --> 01:48:38,310
look clean and professional
3240
01:48:38,320 --> 01:48:40,480
excellent i hope you learn to love data
3241
01:48:40,480 --> 01:48:41,440
visualization
3242
01:48:41,440 --> 01:48:44,080
as much as i do maybe you'll become a
3243
01:48:44,080 --> 01:48:46,320
data visualization pioneer just like
3244
01:48:46,320 --> 01:48:47,920
florence nightingale
3245
01:48:47,920 --> 01:48:50,320
as a budding data analyst you've started
3246
01:48:50,320 --> 01:48:50,960
to feel
3247
01:48:50,960 --> 01:48:53,679
your utility built with valuable tools
3248
01:48:53,679 --> 01:48:54,639
that you'll use
3249
01:48:54,639 --> 01:48:56,880
throughout the rest of the program
3250
01:48:56,880 --> 01:48:58,239
having spreadsheets
3251
01:48:58,239 --> 01:49:01,280
sql and data visualization know-how
3252
01:49:01,280 --> 01:49:04,560
will help make you an ace data detective
3253
01:49:04,560 --> 01:49:06,560
you'll be able to use these tools
3254
01:49:06,560 --> 01:49:08,800
throughout the data analytics process
3255
01:49:08,800 --> 01:49:12,080
as you move forward coming up next
3256
01:49:12,080 --> 01:49:14,320
you'll complete a few activities to wrap
3257
01:49:14,320 --> 01:49:16,080
up this part of the program
3258
01:49:16,080 --> 01:49:18,239
you'll also complete an assessment to
3259
01:49:18,239 --> 01:49:19,440
check your understanding
3260
01:49:19,440 --> 01:49:22,239
of all that you learn this is a great
3261
01:49:22,239 --> 01:49:23,040
opportunity
3262
01:49:23,040 --> 01:49:24,880
to think about some of the areas that
3263
01:49:24,880 --> 01:49:26,560
you'll continue to explore
3264
01:49:26,560 --> 01:49:29,599
in this course and in your career
3265
01:49:29,599 --> 01:49:32,239
as always feel free to review the videos
3266
01:49:32,239 --> 01:49:33,119
and readings
3267
01:49:33,119 --> 01:49:35,520
to help remind you of certain topics and
3268
01:49:35,520 --> 01:49:36,480
ideas
3269
01:49:36,480 --> 01:49:39,599
even if you already feel prepared you're
3270
01:49:39,599 --> 01:49:41,360
just a few steps away from the next
3271
01:49:41,360 --> 01:49:42,159
course
3272
01:49:42,159 --> 01:49:49,910
that's great progress keep it up
3273
01:49:49,920 --> 01:49:52,560
my name is lyla jones and i am a part of
3274
01:49:52,560 --> 01:49:53,760
our cloud team
3275
01:49:53,760 --> 01:49:56,800
i get a chance to lead a team of amazing
3276
01:49:56,800 --> 01:49:58,639
individuals that are focused on helping
3277
01:49:58,639 --> 01:50:00,000
customers get to the cloud
3278
01:50:00,000 --> 01:50:03,520
data visualizations that's a long word
3279
01:50:03,520 --> 01:50:05,599
and that can also make your eyes glaze
3280
01:50:05,599 --> 01:50:07,360
over but i wonder
3281
01:50:07,360 --> 01:50:09,520
if when you were little and you were
3282
01:50:09,520 --> 01:50:10,880
with your parents maybe they had a
3283
01:50:10,880 --> 01:50:12,480
bedtime routine or maybe you have
3284
01:50:12,480 --> 01:50:13,920
children you're doing a bedtime routine
3285
01:50:13,920 --> 01:50:14,560
with them
3286
01:50:14,560 --> 01:50:16,000
you very rarely are going to come to
3287
01:50:16,000 --> 01:50:17,920
those children with a bunch
3288
01:50:17,920 --> 01:50:20,159
of facts and figures before they go to
3289
01:50:20,159 --> 01:50:22,400
bed but i bet you probably are telling
3290
01:50:22,400 --> 01:50:23,360
them a story
3291
01:50:23,360 --> 01:50:24,880
you're showing them pictures i know i
3292
01:50:24,880 --> 01:50:26,719
always loved comic books
3293
01:50:26,719 --> 01:50:29,119
pictures tell a story data
3294
01:50:29,119 --> 01:50:30,320
visualizations
3295
01:50:30,320 --> 01:50:33,360
are pictures they are a wonderful way to
3296
01:50:33,360 --> 01:50:35,760
take very basic ideas around data
3297
01:50:35,760 --> 01:50:39,599
and data points and make them come alive
3298
01:50:39,599 --> 01:50:43,199
you can do all different types of
3299
01:50:43,199 --> 01:50:45,679
combinations of visualizations but the
3300
01:50:45,679 --> 01:50:47,280
ones that are interactive
3301
01:50:47,280 --> 01:50:49,679
wow those are huge can you imagine being
3302
01:50:49,679 --> 01:50:51,280
an executive in organization
3303
01:50:51,280 --> 01:50:53,040
and trying to figure out wow should we
3304
01:50:53,040 --> 01:50:55,360
open up another
3305
01:50:55,360 --> 01:50:58,400
site in in bangkok does that make sense
3306
01:50:58,400 --> 01:51:00,239
and us being able to walk in and saying
3307
01:51:00,239 --> 01:51:01,760
here's why it makes sense and having
3308
01:51:01,760 --> 01:51:03,760
great data visualizations to support all
3309
01:51:03,760 --> 01:51:05,119
of our points of view
3310
01:51:05,119 --> 01:51:07,440
makes it a no-brainer interestingly
3311
01:51:07,440 --> 01:51:09,760
enough i do recall the first time i came
3312
01:51:09,760 --> 01:51:10,480
across
3313
01:51:10,480 --> 01:51:13,119
a super amazing visualization it was in
3314
01:51:13,119 --> 01:51:14,560
my personal life
3315
01:51:14,560 --> 01:51:16,400
i switched my budgeting software from
3316
01:51:16,400 --> 01:51:18,480
one provider to another
3317
01:51:18,480 --> 01:51:20,480
and the provider that i switched to was
3318
01:51:20,480 --> 01:51:22,639
really focused on
3319
01:51:22,639 --> 01:51:24,719
every dollar has a job and making sure
3320
01:51:24,719 --> 01:51:26,560
you're budgeting every single dollar
3321
01:51:26,560 --> 01:51:28,880
they gave visualizations that change
3322
01:51:28,880 --> 01:51:31,119
depending on what input you would add to
3323
01:51:31,119 --> 01:51:31,599
it
3324
01:51:31,599 --> 01:51:33,360
and it really just changed my entire
3325
01:51:33,360 --> 01:51:34,800
perspective the entire thing
3326
01:51:34,800 --> 01:51:37,360
so having the data is like having the
3327
01:51:37,360 --> 01:51:39,119
answer sheet for a test
3328
01:51:39,119 --> 01:51:40,639
it really just lets you know that you're
3329
01:51:40,639 --> 01:51:42,320
going to make good decisions because
3330
01:51:42,320 --> 01:51:50,390
it's backed up by data
3331
01:51:50,400 --> 01:51:52,400
hi in this video we're gonna be taking a
3332
01:51:52,400 --> 01:51:54,400
look how you can access quick labs
3333
01:51:54,400 --> 01:51:56,800
from within your coursera user interface
3334
01:51:56,800 --> 01:51:58,080
let's take a look
3335
01:51:58,080 --> 01:51:59,679
here we are and you're working your way
3336
01:51:59,679 --> 01:52:01,360
through a course and you see
3337
01:52:01,360 --> 01:52:03,440
inside of your left side bar a graded
3338
01:52:03,440 --> 01:52:04,400
external tool
3339
01:52:04,400 --> 01:52:06,000
now first of all you should get excited
3340
01:52:06,000 --> 01:52:06,880
that's where you're actually going to
3341
01:52:06,880 --> 01:52:08,560
apply a lot of these concepts that you
3342
01:52:08,560 --> 01:52:09,199
learned
3343
01:52:09,199 --> 01:52:11,440
hands-on inside of our quick labs but
3344
01:52:11,440 --> 01:52:12,719
how do you get from corsair to quick
3345
01:52:12,719 --> 01:52:13,280
labs
3346
01:52:13,280 --> 01:52:14,480
it's pretty easy it'll take us just
3347
01:52:14,480 --> 01:52:16,719
about a minute so what you're gonna do
3348
01:52:16,719 --> 01:52:19,280
you land on this page we have some tips
3349
01:52:19,280 --> 01:52:20,719
and tricks here for you and i'll walk
3350
01:52:20,719 --> 01:52:22,960
you through one key important one
3351
01:52:22,960 --> 01:52:24,480
the number one thing that i normally get
3352
01:52:24,480 --> 01:52:25,840
wrong when i'm recording these videos or
3353
01:52:25,840 --> 01:52:27,599
taking coursera courses with quick labs
3354
01:52:27,599 --> 01:52:28,159
myself
3355
01:52:28,159 --> 01:52:29,040
is that you want to make sure that
3356
01:52:29,040 --> 01:52:30,800
you're in a private browsing or an
3357
01:52:30,800 --> 01:52:32,400
incognito mode window
3358
01:52:32,400 --> 01:52:34,960
inside of your browser why would you
3359
01:52:34,960 --> 01:52:35,920
want to do that
3360
01:52:35,920 --> 01:52:37,360
well if you're logged into corsair with
3361
01:52:37,360 --> 01:52:39,040
incognito and keep in mind you might
3362
01:52:39,040 --> 01:52:40,400
have to go through a captcha
3363
01:52:40,400 --> 01:52:41,920
by selecting some images to make sure
3364
01:52:41,920 --> 01:52:43,199
that you're not a robot before you go
3365
01:52:43,199 --> 01:52:44,960
through it allows you not to
3366
01:52:44,960 --> 01:52:46,400
accidentally make the mistake
3367
01:52:46,400 --> 01:52:49,040
of logging into quick labs or coursera
3368
01:52:49,040 --> 01:52:51,760
with any email that you don't intend to
3369
01:52:51,760 --> 01:52:53,119
so once you're here and making sure that
3370
01:52:53,119 --> 01:52:54,639
you're in an incognito window and if
3371
01:52:54,639 --> 01:52:55,760
you're using chrome in the upper right
3372
01:52:55,760 --> 01:52:57,280
hand corner you can just verify there's
3373
01:52:57,280 --> 01:52:59,040
this little incognito icon here then
3374
01:52:59,040 --> 01:53:00,400
you're good to go
3375
01:53:00,400 --> 01:53:01,679
all you got to do is scroll down to the
3376
01:53:01,679 --> 01:53:03,440
bottom
3377
01:53:03,440 --> 01:53:05,840
click the i understand box add your
3378
01:53:05,840 --> 01:53:06,880
initials
3379
01:53:06,880 --> 01:53:08,080
and the most important thing that you're
3380
01:53:08,080 --> 01:53:10,400
going to see is open tool
3381
01:53:10,400 --> 01:53:11,599
in the lower right hand corner this
3382
01:53:11,599 --> 01:53:14,320
button will become a nice dark blue once
3383
01:53:14,320 --> 01:53:15,760
you've checked that button
3384
01:53:15,760 --> 01:53:17,360
you open the tool and you'll see a new
3385
01:53:17,360 --> 01:53:19,520
browser window come up
3386
01:53:19,520 --> 01:53:20,719
now unless you want to hear my voice
3387
01:53:20,719 --> 01:53:22,480
again you can just click skip this video
3388
01:53:22,480 --> 01:53:23,760
because that's just an introduction
3389
01:53:23,760 --> 01:53:25,199
there's also an x in the upper right
3390
01:53:25,199 --> 01:53:26,960
hand corner for you
3391
01:53:26,960 --> 01:53:28,159
which will then take you into the quick
3392
01:53:28,159 --> 01:53:30,159
lab itself so to do your work you should
3393
01:53:30,159 --> 01:53:30,639
now have
3394
01:53:30,639 --> 01:53:33,040
two different tabs in your browser one
3395
01:53:33,040 --> 01:53:34,400
has all the video lectures inside of
3396
01:53:34,400 --> 01:53:35,440
coursera
3397
01:53:35,440 --> 01:53:37,199
and the other has the actual hands-on
3398
01:53:37,199 --> 01:53:38,639
quick lab and that's gonna be the
3399
01:53:38,639 --> 01:53:40,080
subject of our next video
3400
01:53:40,080 --> 01:53:41,360
where you learn all about how you can
3401
01:53:41,360 --> 01:53:43,440
get credit for your work done inside the
3402
01:53:43,440 --> 01:53:44,320
quick labs
3403
01:53:44,320 --> 01:53:55,760
we'll see you there
3404
01:53:55,760 --> 01:53:58,000
welcome to quick labs now it's time to
3405
01:53:58,000 --> 01:53:59,280
get hands-on practice
3406
01:53:59,280 --> 01:54:00,960
and a lot of the amazing data analyst
3407
01:54:00,960 --> 01:54:02,719
concepts that you've learned so far
3408
01:54:02,719 --> 01:54:04,080
here's where you get to prove that
3409
01:54:04,080 --> 01:54:05,840
you've learned some great technologies
3410
01:54:05,840 --> 01:54:07,119
and then you'll get credit for it back
3411
01:54:07,119 --> 01:54:09,040
inside of coursera but before you jump
3412
01:54:09,040 --> 01:54:09,599
right in
3413
01:54:09,599 --> 01:54:10,960
let me just give you a quick walkthrough
3414
01:54:10,960 --> 01:54:12,639
of what a quick lab is
3415
01:54:12,639 --> 01:54:14,880
and then we'll get started first and
3416
01:54:14,880 --> 01:54:16,800
foremost in the upper left-hand corner
3417
01:54:16,800 --> 01:54:19,040
you'll notice a gigantic tempting button
3418
01:54:19,040 --> 01:54:20,960
called start lab in green
3419
01:54:20,960 --> 01:54:23,840
i want to go ahead and start that click
3420
01:54:23,840 --> 01:54:26,880
this box that says i'm not a robot
3421
01:54:26,880 --> 01:54:28,320
select a few of the different items for
3422
01:54:28,320 --> 01:54:29,840
your for capture you can see this is the
3423
01:54:29,840 --> 01:54:31,360
hardest part of the lab is trying to
3424
01:54:31,360 --> 01:54:32,719
actually get through the anti-robot
3425
01:54:32,719 --> 01:54:34,639
technology that they have here
3426
01:54:34,639 --> 01:54:36,159
now a couple of things happen in the
3427
01:54:36,159 --> 01:54:38,480
background as author of these labs
3428
01:54:38,480 --> 01:54:40,480
this is some of our best work that we're
3429
01:54:40,480 --> 01:54:43,280
getting you hands-on practice for
3430
01:54:43,280 --> 01:54:44,880
and what we're going to do next is just
3431
01:54:44,880 --> 01:54:46,239
make sure that that timer starts
3432
01:54:46,239 --> 01:54:47,040
counting down
3433
01:54:47,040 --> 01:54:48,000
because then that's going to give you
3434
01:54:48,000 --> 01:54:50,000
the account logins that you need to
3435
01:54:50,000 --> 01:54:51,920
practice the work inside the lab
3436
01:54:51,920 --> 01:54:53,679
now here's what you're gonna do there's
3437
01:54:53,679 --> 01:54:55,599
gonna be a gigantic big button that says
3438
01:54:55,599 --> 01:54:58,159
open google cloud console i want you to
3439
01:54:58,159 --> 01:55:01,910
go ahead and click on that
3440
01:55:01,920 --> 01:55:03,599
and now you have three browser tab
3441
01:55:03,599 --> 01:55:05,599
windows open or a hundred if you're like
3442
01:55:05,599 --> 01:55:06,400
me
3443
01:55:06,400 --> 01:55:08,639
and in the second browser this is your
3444
01:55:08,639 --> 01:55:10,639
quick labs login
3445
01:55:10,639 --> 01:55:12,080
all the way under username what i want
3446
01:55:12,080 --> 01:55:14,159
you to do is copy that username and
3447
01:55:14,159 --> 01:55:15,599
here's the number one mistake that i've
3448
01:55:15,599 --> 01:55:16,800
seen students make
3449
01:55:16,800 --> 01:55:18,320
when you're back into google everyone
3450
01:55:18,320 --> 01:55:20,400
sees this google sign in screen here
3451
01:55:20,400 --> 01:55:21,599
and they immediately start typing in
3452
01:55:21,599 --> 01:55:23,280
their personal gmail address you want to
3453
01:55:23,280 --> 01:55:24,639
be charged for any of the resources that
3454
01:55:24,639 --> 01:55:25,520
you can be using
3455
01:55:25,520 --> 01:55:26,880
that's why we're providing you with this
3456
01:55:26,880 --> 01:55:28,560
quick lapse account yourself
3457
01:55:28,560 --> 01:55:31,199
all i'm going to do is paste in the
3458
01:55:31,199 --> 01:55:32,719
email that's been given to me by the
3459
01:55:32,719 --> 01:55:33,520
quick lab
3460
01:55:33,520 --> 01:55:36,239
for a use for an hour and then click
3461
01:55:36,239 --> 01:55:37,679
next
3462
01:55:37,679 --> 01:55:38,800
and then i'm going to go ahead and grab
3463
01:55:38,800 --> 01:55:41,040
the password
3464
01:55:41,040 --> 01:55:44,390
paste that in here
3465
01:55:44,400 --> 01:55:45,360
and then you're going to see a bunch of
3466
01:55:45,360 --> 01:55:46,960
terms and conditions because this is a
3467
01:55:46,960 --> 01:55:47,840
brand new
3468
01:55:47,840 --> 01:55:49,679
account scroll through the terms and
3469
01:55:49,679 --> 01:55:51,679
conditions read them at your leisure
3470
01:55:51,679 --> 01:55:55,040
click accept and as you work through the
3471
01:55:55,040 --> 01:55:56,080
terms of service here
3472
01:55:56,080 --> 01:55:58,960
eventually you'll land on the homepage
3473
01:55:58,960 --> 01:55:59,280
for
3474
01:55:59,280 --> 01:56:02,239
google cloud scroll down read the terms
3475
01:56:02,239 --> 01:56:02,800
of service
3476
01:56:02,800 --> 01:56:06,320
accept terms of service all the steps
3477
01:56:06,320 --> 01:56:08,239
that i'm walking you through right now
3478
01:56:08,239 --> 01:56:11,040
are also available inside of the lab
3479
01:56:11,040 --> 01:56:13,199
written instructions as well
3480
01:56:13,199 --> 01:56:14,400
so once you've gotten that out of the
3481
01:56:14,400 --> 01:56:16,320
way let's take a look at what this
3482
01:56:16,320 --> 01:56:18,000
particular quick lab is going to teach
3483
01:56:18,000 --> 01:56:18,960
you
3484
01:56:18,960 --> 01:56:20,159
at a high level i'm not going to do the
3485
01:56:20,159 --> 01:56:21,920
lab for you but don't worry it's step by
3486
01:56:21,920 --> 01:56:23,440
step you'll have a blast doing it
3487
01:56:23,440 --> 01:56:25,040
and you have way more than enough time
3488
01:56:25,040 --> 01:56:26,800
you need in this timer before your lab
3489
01:56:26,800 --> 01:56:28,000
timer runs out
3490
01:56:28,000 --> 01:56:29,840
generally as lab authors we try to
3491
01:56:29,840 --> 01:56:31,440
double the budget of allowed time
3492
01:56:31,440 --> 01:56:33,920
so don't worry about lab timeouts so
3493
01:56:33,920 --> 01:56:34,880
inside the lab
3494
01:56:34,880 --> 01:56:36,880
every lab will start with an overview
3495
01:56:36,880 --> 01:56:38,400
and then what specifically it is that
3496
01:56:38,400 --> 01:56:39,520
you're going to do
3497
01:56:39,520 --> 01:56:41,040
inside this lab since you're a data
3498
01:56:41,040 --> 01:56:43,280
analyst you'll be creating spreadsheets
3499
01:56:43,280 --> 01:56:45,360
and adding files and sharing files with
3500
01:56:45,360 --> 01:56:47,040
some of your other data analysts
3501
01:56:47,040 --> 01:56:48,560
if you forget what section you're in
3502
01:56:48,560 --> 01:56:50,239
another pro tip that i like to do is
3503
01:56:50,239 --> 01:56:51,679
on the right hand side you can actually
3504
01:56:51,679 --> 01:56:53,119
click between each of the different
3505
01:56:53,119 --> 01:56:54,639
header sections right here as i'm doing
3506
01:56:54,639 --> 01:56:55,360
now
3507
01:56:55,360 --> 01:56:57,119
and you can jump to a particular section
3508
01:56:57,119 --> 01:56:58,960
inside of the workbook so say you and
3509
01:56:58,960 --> 01:56:59,840
your friends are working on this
3510
01:56:59,840 --> 01:57:00,480
together
3511
01:57:00,480 --> 01:57:02,320
you can say hey i'm having trouble on
3512
01:57:02,320 --> 01:57:03,760
the share and collaborate section
3513
01:57:03,760 --> 01:57:05,119
you can just click on that and it'll
3514
01:57:05,119 --> 01:57:07,360
jump right to that section
3515
01:57:07,360 --> 01:57:08,719
well that's pretty much it a few more
3516
01:57:08,719 --> 01:57:10,000
housekeeping items and then you're off
3517
01:57:10,000 --> 01:57:11,599
to the races
3518
01:57:11,599 --> 01:57:14,480
as you work your way through the lab you
3519
01:57:14,480 --> 01:57:15,599
want to make sure that you're completing
3520
01:57:15,599 --> 01:57:16,560
the objectives
3521
01:57:16,560 --> 01:57:17,840
and making sure that you're staying on
3522
01:57:17,840 --> 01:57:19,840
track there is intelligence built into
3523
01:57:19,840 --> 01:57:20,639
quick labs
3524
01:57:20,639 --> 01:57:22,960
to prevent any kind of behavior that's
3525
01:57:22,960 --> 01:57:24,480
not part of the lab so make sure you're
3526
01:57:24,480 --> 01:57:25,760
following the lab as is
3527
01:57:25,760 --> 01:57:27,760
and when you're done with the lab say i
3528
01:57:27,760 --> 01:57:29,199
was completed here
3529
01:57:29,199 --> 01:57:31,440
all you have to do is click on end lab
3530
01:57:31,440 --> 01:57:33,440
click on ok
3531
01:57:33,440 --> 01:57:34,400
and that's going to bring you to a
3532
01:57:34,400 --> 01:57:36,159
pop-up screen that asks you to rate the
3533
01:57:36,159 --> 01:57:36,960
lab
3534
01:57:36,960 --> 01:57:38,800
add in any comments for the lab authors
3535
01:57:38,800 --> 01:57:40,000
like me
3536
01:57:40,000 --> 01:57:42,159
submit it and then automatically your
3537
01:57:42,159 --> 01:57:43,520
score for the work that you've done in
3538
01:57:43,520 --> 01:57:44,080
the lab
3539
01:57:44,080 --> 01:57:46,480
is fed back to coursera which is pretty
3540
01:57:46,480 --> 01:57:47,199
cool
3541
01:57:47,199 --> 01:57:49,199
and that's it that was a quick tour of a
3542
01:57:49,199 --> 01:57:50,800
quick lab is about five minutes
3543
01:57:50,800 --> 01:57:52,320
but honestly you'll have a blast going
3544
01:57:52,320 --> 01:58:01,530
through these good luck
3545
01:58:01,540 --> 01:58:04,639
[Music]
3546
01:58:04,639 --> 01:58:06,080
next we're going to cover a really
3547
01:58:06,080 --> 01:58:08,159
important topic which is how to get help
3548
01:58:08,159 --> 01:58:09,840
with quick lab should you need it
3549
01:58:09,840 --> 01:58:11,040
and to do that we're going to jump back
3550
01:58:11,040 --> 01:58:13,360
into our quick labs user interface
3551
01:58:13,360 --> 01:58:15,360
here i am back inside of the quick lab
3552
01:58:15,360 --> 01:58:17,440
inside the quick labs user interface
3553
01:58:17,440 --> 01:58:19,280
if you're working through a lab and just
3554
01:58:19,280 --> 01:58:20,880
something's not acting right or
3555
01:58:20,880 --> 01:58:23,280
the lab instructions don't seem to be
3556
01:58:23,280 --> 01:58:25,040
telling you exactly what you need to do
3557
01:58:25,040 --> 01:58:26,400
we try our hardest to make sure that the
3558
01:58:26,400 --> 01:58:28,400
labs are step by step but at any point
3559
01:58:28,400 --> 01:58:29,599
in the lower right-hand corner you can
3560
01:58:29,599 --> 01:58:32,000
click on the chat bubble once we've done
3561
01:58:32,000 --> 01:58:32,719
that
3562
01:58:32,719 --> 01:58:35,679
add in your name add in your email and
3563
01:58:35,679 --> 01:58:36,159
you can
3564
01:58:36,159 --> 01:58:37,840
optionally define the department for
3565
01:58:37,840 --> 01:58:40,470
quick labs
3566
01:58:40,480 --> 01:58:41,679
i'm going to say quick lab general
3567
01:58:41,679 --> 01:58:43,840
support and you can start a chat so let
3568
01:58:43,840 --> 01:58:48,480
me go ahead and add my name
3569
01:58:48,480 --> 01:58:53,589
add my email
3570
01:58:53,599 --> 01:58:56,239
and start chat and then this window will
3571
01:58:56,239 --> 01:58:57,280
pop up
3572
01:58:57,280 --> 01:58:58,400
now this gets you connected with the
3573
01:58:58,400 --> 01:59:00,400
quick labs live support representative
3574
01:59:00,400 --> 01:59:01,760
they're amazing they're friendly they've
3575
01:59:01,760 --> 01:59:03,679
been doing this for years and likely
3576
01:59:03,679 --> 01:59:04,639
your problem has already been
3577
01:59:04,639 --> 01:59:06,320
encountered by somebody before
3578
01:59:06,320 --> 01:59:08,320
so don't hesitate drop them a line and
3579
01:59:08,320 --> 01:59:09,360
if they're out of office
3580
01:59:09,360 --> 01:59:10,880
it'll automatically create a support
3581
01:59:10,880 --> 01:59:12,719
ticket where they'll follow up with you
3582
01:59:12,719 --> 01:59:13,840
from that email address that you
3583
01:59:13,840 --> 01:59:16,560
provided that's it have fun working
3584
01:59:16,560 --> 01:59:17,920
through your labs and earning that
3585
01:59:17,920 --> 01:59:24,790
credit
3586
01:59:24,800 --> 01:59:27,679
hey great to have you back now it's time
3587
01:59:27,679 --> 01:59:29,280
to get down to business
3588
01:59:29,280 --> 01:59:30,639
we're going to start talking about
3589
01:59:30,639 --> 01:59:32,400
practical ways businesses
3590
01:59:32,400 --> 01:59:34,639
are using data and the opportunities
3591
01:59:34,639 --> 01:59:36,159
that it can create for you
3592
01:59:36,159 --> 01:59:38,320
so far you've learned a lot of practical
3593
01:59:38,320 --> 01:59:40,159
data analysis skills
3594
01:59:40,159 --> 01:59:42,159
with these next couple of videos we're
3595
01:59:42,159 --> 01:59:43,599
going to switch gears a little
3596
01:59:43,599 --> 01:59:44,960
and talk about why you're learning these
3597
01:59:44,960 --> 01:59:47,119
skills hopefully this will give you more
3598
01:59:47,119 --> 01:59:48,480
perspective into what kinds of
3599
01:59:48,480 --> 01:59:50,320
opportunities are out there for you
3600
01:59:50,320 --> 01:59:52,400
coming up we're going to talk more about
3601
01:59:52,400 --> 01:59:54,560
the kinds of roles data analysts play in
3602
01:59:54,560 --> 01:59:55,760
different industries
3603
01:59:55,760 --> 01:59:57,920
the tasks that these roles require and
3604
01:59:57,920 --> 02:00:00,000
the importance of fairness and avoiding
3605
02:00:00,000 --> 02:00:00,480
bias
3606
02:00:00,480 --> 02:00:02,719
and analyzing data for business tasks
3607
02:00:02,719 --> 02:00:04,560
we'll also talk about opportunities
3608
02:00:04,560 --> 02:00:06,639
you can tap into and how this program
3609
02:00:06,639 --> 02:00:08,480
factors into your future success
3610
02:00:08,480 --> 02:00:10,880
in the data analyst role so with all
3611
02:00:10,880 --> 02:00:14,870
that in mind let's get started
3612
02:00:14,880 --> 02:00:17,360
previously we learned about what a data
3613
02:00:17,360 --> 02:00:18,480
analyst does
3614
02:00:18,480 --> 02:00:21,199
and why that work is so valuable now
3615
02:00:21,199 --> 02:00:21,920
let's look at
3616
02:00:21,920 --> 02:00:23,920
where data analysts actually do their
3617
02:00:23,920 --> 02:00:26,320
work you'll learn much more about the
3618
02:00:26,320 --> 02:00:27,119
industries
3619
02:00:27,119 --> 02:00:29,520
you could work in as a data analyst and
3620
02:00:29,520 --> 02:00:31,199
how companies in these fields are
3621
02:00:31,199 --> 02:00:32,960
already using data analytics
3622
02:00:32,960 --> 02:00:35,199
to do some really cool things there are
3623
02:00:35,199 --> 02:00:37,040
so many businesses out there
3624
02:00:37,040 --> 02:00:38,960
that have a big need for the skills
3625
02:00:38,960 --> 02:00:40,639
you're learning right now
3626
02:00:40,639 --> 02:00:42,719
across industries like technology
3627
02:00:42,719 --> 02:00:44,239
marketing finance
3628
02:00:44,239 --> 02:00:46,560
health care and so many more real
3629
02:00:46,560 --> 02:00:48,400
companies are already using data
3630
02:00:48,400 --> 02:00:49,040
analytics
3631
02:00:49,040 --> 02:00:51,280
to stay ahead of the curve and the more
3632
02:00:51,280 --> 02:00:53,360
they use data in their business
3633
02:00:53,360 --> 02:00:55,199
the more they understand just how
3634
02:00:55,199 --> 02:00:56,560
important data analysts
3635
02:00:56,560 --> 02:00:59,520
like you are to their success let's look
3636
02:00:59,520 --> 02:01:01,119
at a real life example
3637
02:01:01,119 --> 02:01:03,920
of a brand you'll probably recognize
3638
02:01:03,920 --> 02:01:05,599
coca-cola
3639
02:01:05,599 --> 02:01:07,679
data is changing the way coca-cola
3640
02:01:07,679 --> 02:01:09,920
approaches its marketing strategies
3641
02:01:09,920 --> 02:01:12,320
coca-cola uses data gathered from
3642
02:01:12,320 --> 02:01:13,679
consumer feedback
3643
02:01:13,679 --> 02:01:15,520
to create advertising that speaks
3644
02:01:15,520 --> 02:01:17,679
directly to different audiences
3645
02:01:17,679 --> 02:01:19,920
with different interests how does this
3646
02:01:19,920 --> 02:01:22,239
work you know those high-tech coca-cola
3647
02:01:22,239 --> 02:01:23,199
vending machines
3648
02:01:23,199 --> 02:01:25,760
you see at movie theater sometimes it's
3649
02:01:25,760 --> 02:01:27,520
always fun getting to make your own
3650
02:01:27,520 --> 02:01:28,639
flavors
3651
02:01:28,639 --> 02:01:30,639
well those machines have built-in
3652
02:01:30,639 --> 02:01:32,080
artificial intelligence
3653
02:01:32,080 --> 02:01:34,400
and data analysis tools this helps
3654
02:01:34,400 --> 02:01:35,199
coca-cola
3655
02:01:35,199 --> 02:01:36,880
see all the different kinds of flavor
3656
02:01:36,880 --> 02:01:39,119
combinations people are coming up with
3657
02:01:39,119 --> 02:01:41,679
which they can then use as inspiration
3658
02:01:41,679 --> 02:01:43,040
for new products
3659
02:01:43,040 --> 02:01:45,520
how cool is that ever wonder how google
3660
02:01:45,520 --> 02:01:46,960
gives you the right answer
3661
02:01:46,960 --> 02:01:49,760
to any question in just seconds that's
3662
02:01:49,760 --> 02:01:51,440
powered by data2
3663
02:01:51,440 --> 02:01:53,760
we use all kinds of data to determine a
3664
02:01:53,760 --> 02:01:55,360
website's reliability
3665
02:01:55,360 --> 02:01:57,440
and accuracy to make sure you get the
3666
02:01:57,440 --> 02:01:58,800
most useful results
3667
02:01:58,800 --> 02:02:01,119
for any search you make but it isn't
3668
02:02:01,119 --> 02:02:03,360
just big companies like coca-cola
3669
02:02:03,360 --> 02:02:06,000
and google that use data small
3670
02:02:06,000 --> 02:02:06,719
businesses
3671
02:02:06,719 --> 02:02:08,800
everywhere are also starting to take
3672
02:02:08,800 --> 02:02:11,199
advantage of data-driven insights
3673
02:02:11,199 --> 02:02:13,199
to improve their operations and make
3674
02:02:13,199 --> 02:02:14,480
better decisions
3675
02:02:14,480 --> 02:02:17,280
small businesses can use data to do all
3676
02:02:17,280 --> 02:02:18,800
kinds of things
3677
02:02:18,800 --> 02:02:20,880
they might use data analytics to better
3678
02:02:20,880 --> 02:02:23,119
understand their customers buying habits
3679
02:02:23,119 --> 02:02:24,800
create more effective social media
3680
02:02:24,800 --> 02:02:26,320
messaging or
3681
02:02:26,320 --> 02:02:28,960
in the case of one city zoo and aquarium
3682
02:02:28,960 --> 02:02:31,199
predict the number of daily visitors
3683
02:02:31,199 --> 02:02:33,840
based on local climate data city zoo and
3684
02:02:33,840 --> 02:02:35,360
aquarium realized that
3685
02:02:35,360 --> 02:02:37,920
on rainy days they were seeing huge
3686
02:02:37,920 --> 02:02:39,360
drop-offs in attendance
3687
02:02:39,360 --> 02:02:41,199
but they had no way to accurately
3688
02:02:41,199 --> 02:02:43,679
predict when those rainy days would hit
3689
02:02:43,679 --> 02:02:46,320
this made staffing a real challenge some
3690
02:02:46,320 --> 02:02:48,560
days they found themselves overstaffed
3691
02:02:48,560 --> 02:02:50,560
other days they were unprepared for the
3692
02:02:50,560 --> 02:02:51,920
rush of visitors
3693
02:02:51,920 --> 02:02:53,920
to deal with this data analysts took
3694
02:02:53,920 --> 02:02:55,760
years of weather records from the zoo
3695
02:02:55,760 --> 02:02:58,159
and used that data to accurately predict
3696
02:02:58,159 --> 02:03:00,000
future weather patterns
3697
02:03:00,000 --> 02:03:02,000
this made it easy for the zoo to know
3698
02:03:02,000 --> 02:03:04,239
how much staff they needed when
3699
02:03:04,239 --> 02:03:06,320
because the zoo could predict and manage
3700
02:03:06,320 --> 02:03:08,320
their staffing needs more accurately
3701
02:03:08,320 --> 02:03:09,920
they were able to provide a better
3702
02:03:09,920 --> 02:03:11,920
experience for visitors and dedicate
3703
02:03:11,920 --> 02:03:12,960
more resources
3704
02:03:12,960 --> 02:03:14,800
to creating a better experience for the
3705
02:03:14,800 --> 02:03:16,639
animals too
3706
02:03:16,639 --> 02:03:18,159
we see a similar thing in the health
3707
02:03:18,159 --> 02:03:19,920
care industry there
3708
02:03:19,920 --> 02:03:22,239
data analysts look at clinic attendance
3709
02:03:22,239 --> 02:03:23,280
data to help
3710
02:03:23,280 --> 02:03:26,080
hospitals and doctors offices predict
3711
02:03:26,080 --> 02:03:27,679
when rush hours will hit
3712
02:03:27,679 --> 02:03:30,239
so they can be ready for it your local
3713
02:03:30,239 --> 02:03:31,119
city hospital
3714
02:03:31,119 --> 02:03:33,520
is a great example let's say they've
3715
02:03:33,520 --> 02:03:35,119
been getting complaints about long wait
3716
02:03:35,119 --> 02:03:36,239
times
3717
02:03:36,239 --> 02:03:38,639
sometimes an hour or more which made it
3718
02:03:38,639 --> 02:03:40,400
hard for some patients to get the care
3719
02:03:40,400 --> 02:03:41,440
they needed
3720
02:03:41,440 --> 02:03:43,360
so data analysts use data about the
3721
02:03:43,360 --> 02:03:45,119
hospital's daily foot traffic
3722
02:03:45,119 --> 02:03:46,480
to help them make more informed
3723
02:03:46,480 --> 02:03:48,880
decisions about how many doctors they
3724
02:03:48,880 --> 02:03:49,280
need
3725
02:03:49,280 --> 02:03:52,480
on staff at any given time this helped
3726
02:03:52,480 --> 02:03:53,920
reduce wait times
3727
02:03:53,920 --> 02:03:56,880
improve their patients experience and
3728
02:03:56,880 --> 02:03:57,920
make better use
3729
02:03:57,920 --> 02:04:00,320
of the healthcare workers time too like
3730
02:04:00,320 --> 02:04:01,040
i said
3731
02:04:01,040 --> 02:04:02,719
there are many ways that companies in
3732
02:04:02,719 --> 02:04:05,040
different industries put data to use
3733
02:04:05,040 --> 02:04:07,119
but they can only do that if they have
3734
02:04:07,119 --> 02:04:08,079
data analysts
3735
02:04:08,079 --> 02:04:10,239
they can rely on so you might be
3736
02:04:10,239 --> 02:04:13,199
wondering how you fit into the equation
3737
02:04:13,199 --> 02:04:16,079
well you've got plenty of options but
3738
02:04:16,079 --> 02:04:17,840
you don't have to decide what industry
3739
02:04:17,840 --> 02:04:18,880
you want to work in
3740
02:04:18,880 --> 02:04:21,360
by the way there will be plenty of time
3741
02:04:21,360 --> 02:04:22,320
to think about that
3742
02:04:22,320 --> 02:04:23,520
as you make your way through this
3743
02:04:23,520 --> 02:04:25,520
program by the time you finish this
3744
02:04:25,520 --> 02:04:26,159
program
3745
02:04:26,159 --> 02:04:27,520
you'll have the core skills that will
3746
02:04:27,520 --> 02:04:29,360
make you valuable in any industry that
3747
02:04:29,360 --> 02:04:31,520
makes data driven decisions
3748
02:04:31,520 --> 02:04:34,800
which as it turns out is most industries
3749
02:04:34,800 --> 02:04:37,599
even zoos coming up we'll check out the
3750
02:04:37,599 --> 02:04:38,000
business
3751
02:04:38,000 --> 02:04:40,719
task where data can be helpful and we'll
3752
02:04:40,719 --> 02:04:41,760
explore even more
3753
02:04:41,760 --> 02:04:43,679
how data analysts are empowering
3754
02:04:43,679 --> 02:04:45,599
businesses through data
3755
02:04:45,599 --> 02:04:52,400
i'll see you then hi
3756
02:04:52,400 --> 02:04:55,199
i'm joey and i work as an analytics
3757
02:04:55,199 --> 02:04:57,599
program manager within ruse
3758
02:04:57,599 --> 02:04:59,599
now ruse stands for real estate and
3759
02:04:59,599 --> 02:05:01,360
workplace services
3760
02:05:01,360 --> 02:05:04,000
and my job is to bring data and
3761
02:05:04,000 --> 02:05:04,880
analytics
3762
02:05:04,880 --> 02:05:07,280
into the decision making here especially
3763
02:05:07,280 --> 02:05:08,480
with regards to
3764
02:05:08,480 --> 02:05:12,000
creating a safe and fun work environment
3765
02:05:12,000 --> 02:05:13,840
my journey into analytics was a bit
3766
02:05:13,840 --> 02:05:15,920
different in that i had no plan
3767
02:05:15,920 --> 02:05:18,239
or really didn't see myself being where
3768
02:05:18,239 --> 02:05:19,119
i am now
3769
02:05:19,119 --> 02:05:21,760
now luckily i started in a rotational
3770
02:05:21,760 --> 02:05:23,840
program called the hra program
3771
02:05:23,840 --> 02:05:25,840
within people operations which afforded
3772
02:05:25,840 --> 02:05:27,040
me the ability
3773
02:05:27,040 --> 02:05:29,040
to play three different roles
3774
02:05:29,040 --> 02:05:30,800
essentially i was
3775
02:05:30,800 --> 02:05:34,159
in a generalist capacity in a specialist
3776
02:05:34,159 --> 02:05:37,119
role and as an analyst and i really
3777
02:05:37,119 --> 02:05:38,639
found a love and a passion
3778
02:05:38,639 --> 02:05:42,000
in the analytical work i started on the
3779
02:05:42,000 --> 02:05:43,440
business intelligence team
3780
02:05:43,440 --> 02:05:46,400
whose job was to provide sql based
3781
02:05:46,400 --> 02:05:48,400
reporting back to the business
3782
02:05:48,400 --> 02:05:51,040
i realized that analytics is the right
3783
02:05:51,040 --> 02:05:52,159
career path for me
3784
02:05:52,159 --> 02:05:54,560
when i found myself enjoying coming to
3785
02:05:54,560 --> 02:05:57,119
work and getting my work done
3786
02:05:57,119 --> 02:05:59,280
and i think i can connect that to two
3787
02:05:59,280 --> 02:06:00,239
passions of mine
3788
02:06:00,239 --> 02:06:02,800
the first is problem solving i love
3789
02:06:02,800 --> 02:06:03,840
taking a complex
3790
02:06:03,840 --> 02:06:06,159
problem a mystery a riddle and being
3791
02:06:06,159 --> 02:06:06,960
able to
3792
02:06:06,960 --> 02:06:09,040
find the answers and come up with a
3793
02:06:09,040 --> 02:06:10,480
solution
3794
02:06:10,480 --> 02:06:12,159
and then the second thing is being able
3795
02:06:12,159 --> 02:06:14,000
to work with people and help people
3796
02:06:14,000 --> 02:06:15,840
in analytics i feel like the key to
3797
02:06:15,840 --> 02:06:18,079
success is being able to blend
3798
02:06:18,079 --> 02:06:20,400
the personal side with the technical
3799
02:06:20,400 --> 02:06:21,520
side
3800
02:06:21,520 --> 02:06:23,440
at the beginning of my career i focused
3801
02:06:23,440 --> 02:06:25,440
a little more on the technical pieces
3802
02:06:25,440 --> 02:06:27,440
and i wanted to make sure i had the
3803
02:06:27,440 --> 02:06:29,280
right technical knowledge to be able to
3804
02:06:29,280 --> 02:06:30,800
answer questions
3805
02:06:30,800 --> 02:06:33,679
but what i found is over time i needed
3806
02:06:33,679 --> 02:06:36,079
to grow that other side just as much
3807
02:06:36,079 --> 02:06:38,480
and i think that my career has allowed
3808
02:06:38,480 --> 02:06:39,920
me those opportunities
3809
02:06:39,920 --> 02:06:42,320
to kind of work each of those muscles
3810
02:06:42,320 --> 02:06:44,000
the human interaction part
3811
02:06:44,000 --> 02:06:46,079
and the technical part to make sure that
3812
02:06:46,079 --> 02:06:47,360
they're both growing
3813
02:06:47,360 --> 02:06:54,709
at the end of the day
3814
02:06:54,719 --> 02:06:57,040
for any analyst for any person that's
3815
02:06:57,040 --> 02:06:58,000
honestly at the early
3816
02:06:58,000 --> 02:07:00,320
stages of their career understanding
3817
02:07:00,320 --> 02:07:02,320
data respecting data and knowing how to
3818
02:07:02,320 --> 02:07:04,320
work with data is incredibly important
3819
02:07:04,320 --> 02:07:05,280
because
3820
02:07:05,280 --> 02:07:08,159
you know my vision is that every role in
3821
02:07:08,159 --> 02:07:10,560
some form or fashion will involve data
3822
02:07:10,560 --> 02:07:13,199
and it's used in learning how to extract
3823
02:07:13,199 --> 02:07:14,239
insights from it
3824
02:07:14,239 --> 02:07:16,239
will be at the core of any critical role
3825
02:07:16,239 --> 02:07:18,000
across any company organization
3826
02:07:18,000 --> 02:07:19,760
generally in those first two years
3827
02:07:19,760 --> 02:07:21,920
you're developing the core skill sets
3828
02:07:21,920 --> 02:07:24,079
that make you a fantastic generalist
3829
02:07:24,079 --> 02:07:26,880
and then in the next two to five years
3830
02:07:26,880 --> 02:07:28,320
you're learning about
3831
02:07:28,320 --> 02:07:30,159
something very specific as a as it
3832
02:07:30,159 --> 02:07:31,520
relates to your job so
3833
02:07:31,520 --> 02:07:32,639
whether it's the area that you're
3834
02:07:32,639 --> 02:07:35,199
supporting or maybe a very technical
3835
02:07:35,199 --> 02:07:36,079
component
3836
02:07:36,079 --> 02:07:38,000
like let's say you want to become a sql
3837
02:07:38,000 --> 02:07:39,679
expert so that you can manipulate large
3838
02:07:39,679 --> 02:07:41,280
data sets for financial analysis
3839
02:07:41,280 --> 02:07:42,159
purposes
3840
02:07:42,159 --> 02:07:44,880
similarly even if you come into finance
3841
02:07:44,880 --> 02:07:46,400
as a data analyst
3842
02:07:46,400 --> 02:07:48,960
you can pop out of finance and go into
3843
02:07:48,960 --> 02:07:50,400
what a lot of people like to call the
3844
02:07:50,400 --> 02:07:51,119
business
3845
02:07:51,119 --> 02:07:52,800
which is typically your operations
3846
02:07:52,800 --> 02:07:54,239
functions and become
3847
02:07:54,239 --> 02:07:56,960
a business analyst or a data analyst
3848
02:07:56,960 --> 02:07:58,639
there's so many different paths that you
3849
02:07:58,639 --> 02:08:00,320
can take from the starting point
3850
02:08:00,320 --> 02:08:03,119
that you really can't predict you're in
3851
02:08:03,119 --> 02:08:05,280
i'm just deeply passionate
3852
02:08:05,280 --> 02:08:07,440
about working with and supporting young
3853
02:08:07,440 --> 02:08:09,520
people and really giving them
3854
02:08:09,520 --> 02:08:12,719
a jump start to their career this stems
3855
02:08:12,719 --> 02:08:13,840
from honestly
3856
02:08:13,840 --> 02:08:16,400
my own personal experience where in the
3857
02:08:16,400 --> 02:08:18,400
first two years of my career
3858
02:08:18,400 --> 02:08:20,960
i had essentially zero support from my
3859
02:08:20,960 --> 02:08:23,599
manager and my direct management chains
3860
02:08:23,599 --> 02:08:24,960
having gone through that experience in
3861
02:08:24,960 --> 02:08:26,719
my first two years i
3862
02:08:26,719 --> 02:08:28,560
realized and i felt to experience how
3863
02:08:28,560 --> 02:08:30,400
that can slow you down
3864
02:08:30,400 --> 02:08:32,079
and especially when you're somebody that
3865
02:08:32,079 --> 02:08:33,679
has a lot of potential
3866
02:08:33,679 --> 02:08:35,679
and a lot of ability you want to be able
3867
02:08:35,679 --> 02:08:36,880
to be in an environment
3868
02:08:36,880 --> 02:08:39,040
that fosters that ability and really
3869
02:08:39,040 --> 02:08:40,400
wants to see you grow
3870
02:08:40,400 --> 02:08:42,719
i think it's incredibly important to
3871
02:08:42,719 --> 02:08:44,719
have programs like these
3872
02:08:44,719 --> 02:08:48,079
that take away all the barriers that
3873
02:08:48,079 --> 02:08:50,079
remove any of the constructs that
3874
02:08:50,079 --> 02:08:52,000
prevent people from being able to
3875
02:08:52,000 --> 02:08:55,840
find out what they need um to be
3876
02:08:55,840 --> 02:08:57,440
in an industry like this to be
3877
02:08:57,440 --> 02:09:00,480
successful in a role like a data analyst
3878
02:09:00,480 --> 02:09:02,560
so that they themselves can dream about
3879
02:09:02,560 --> 02:09:04,159
where they can go in their career
3880
02:09:04,159 --> 02:09:06,960
my name is tony i'm a finance program
3881
02:09:06,960 --> 02:09:11,109
manager at google
3882
02:09:11,119 --> 02:09:13,440
as a data analyst you'll be tackling
3883
02:09:13,440 --> 02:09:15,520
business tasks that help companies
3884
02:09:15,520 --> 02:09:18,719
use data coming up we'll talk more about
3885
02:09:18,719 --> 02:09:21,199
what a business task actually is and
3886
02:09:21,199 --> 02:09:22,800
some examples of what they might look
3887
02:09:22,800 --> 02:09:25,360
like in real data analyst jobs
3888
02:09:25,360 --> 02:09:27,280
let's take a second to think back on the
3889
02:09:27,280 --> 02:09:29,440
real examples of businesses using data
3890
02:09:29,440 --> 02:09:30,159
analytics
3891
02:09:30,159 --> 02:09:32,639
and their operation we've seen before
3892
02:09:32,639 --> 02:09:33,679
you might have noticed
3893
02:09:33,679 --> 02:09:36,480
a common theme across every example they
3894
02:09:36,480 --> 02:09:37,119
all have
3895
02:09:37,119 --> 02:09:40,239
issues to explore questions to answer or
3896
02:09:40,239 --> 02:09:42,000
problems to solve
3897
02:09:42,000 --> 02:09:43,920
it's easy for these things to get mixed
3898
02:09:43,920 --> 02:09:46,400
up so here's a way to keep them straight
3899
02:09:46,400 --> 02:09:48,159
when we talk about them in data
3900
02:09:48,159 --> 02:09:51,360
analytics an issue is a topic or subject
3901
02:09:51,360 --> 02:09:53,040
to investigate
3902
02:09:53,040 --> 02:09:55,440
a question is designed to discover
3903
02:09:55,440 --> 02:09:56,639
information
3904
02:09:56,639 --> 02:09:58,880
and a problem is an obstacle or
3905
02:09:58,880 --> 02:09:59,760
complication
3906
02:09:59,760 --> 02:10:02,400
that needs to be worked out coca-cola
3907
02:10:02,400 --> 02:10:04,560
had a question about new products
3908
02:10:04,560 --> 02:10:06,800
data analysis gave them insights into
3909
02:10:06,800 --> 02:10:08,079
new flavors customers
3910
02:10:08,079 --> 02:10:11,199
already like the city zoo and aquarium
3911
02:10:11,199 --> 02:10:14,239
had a problem with staffing data helped
3912
02:10:14,239 --> 02:10:15,520
them figure out the best
3913
02:10:15,520 --> 02:10:18,159
staffing strategy these questions and
3914
02:10:18,159 --> 02:10:18,880
problems
3915
02:10:18,880 --> 02:10:21,040
become the foundation for all kinds of
3916
02:10:21,040 --> 02:10:22,239
business tasks
3917
02:10:22,239 --> 02:10:25,280
that you'll help solve as a data analyst
3918
02:10:25,280 --> 02:10:27,520
a business task is the question or
3919
02:10:27,520 --> 02:10:28,320
problem
3920
02:10:28,320 --> 02:10:31,520
data analysis answers for business this
3921
02:10:31,520 --> 02:10:33,040
is where you'll focus a lot of your
3922
02:10:33,040 --> 02:10:33,520
efforts
3923
02:10:33,520 --> 02:10:35,040
in the work you'll do for future
3924
02:10:35,040 --> 02:10:37,119
employers let's stick with our zoo
3925
02:10:37,119 --> 02:10:37,760
example
3926
02:10:37,760 --> 02:10:39,360
and see if we can imagine what a
3927
02:10:39,360 --> 02:10:42,400
business task for a zoo might look like
3928
02:10:42,400 --> 02:10:44,400
we know the problem unpredictable
3929
02:10:44,400 --> 02:10:46,480
weather was making it hard for the zoo
3930
02:10:46,480 --> 02:10:48,880
to anticipate staffing needs
3931
02:10:48,880 --> 02:10:51,199
so maybe the business task could be
3932
02:10:51,199 --> 02:10:52,079
something like
3933
02:10:52,079 --> 02:10:54,239
analyze weather data from the last
3934
02:10:54,239 --> 02:10:55,599
decade to identify
3935
02:10:55,599 --> 02:10:58,320
predictable patterns the data analysts
3936
02:10:58,320 --> 02:11:00,079
could then plan out the best way
3937
02:11:00,079 --> 02:11:02,880
to gather analyze and present the data
3938
02:11:02,880 --> 02:11:04,560
needed to solve this task
3939
02:11:04,560 --> 02:11:07,280
and meet the zoo's goals then using data
3940
02:11:07,280 --> 02:11:08,880
the zoo would be able to make informed
3941
02:11:08,880 --> 02:11:09,599
decisions
3942
02:11:09,599 --> 02:11:12,079
about their daily staffing so we talked
3943
02:11:12,079 --> 02:11:12,800
a little about
3944
02:11:12,800 --> 02:11:14,800
data-driven decision-making in previous
3945
02:11:14,800 --> 02:11:15,920
videos
3946
02:11:15,920 --> 02:11:18,320
but just in case you need a refresher
3947
02:11:18,320 --> 02:11:19,360
here it is
3948
02:11:19,360 --> 02:11:21,599
data-driven decision-making is when
3949
02:11:21,599 --> 02:11:23,599
facts that have been discovered through
3950
02:11:23,599 --> 02:11:24,000
data
3951
02:11:24,000 --> 02:11:26,400
analysis are used to guide business
3952
02:11:26,400 --> 02:11:27,280
strategy
3953
02:11:27,280 --> 02:11:28,960
the simplest way to think about decision
3954
02:11:28,960 --> 02:11:31,199
making is that it's a choice between
3955
02:11:31,199 --> 02:11:32,560
consequences
3956
02:11:32,560 --> 02:11:35,760
good bad or a combination of both
3957
02:11:35,760 --> 02:11:38,320
in our zoo example the zoo had the data
3958
02:11:38,320 --> 02:11:39,199
they needed
3959
02:11:39,199 --> 02:11:41,520
to make an informed decision that solved
3960
02:11:41,520 --> 02:11:42,480
their problem
3961
02:11:42,480 --> 02:11:44,159
but what if they had made this decision
3962
02:11:44,159 --> 02:11:45,520
without data
3963
02:11:45,520 --> 02:11:47,520
let's say they just relied on
3964
02:11:47,520 --> 02:11:49,599
observation and memory to track the
3965
02:11:49,599 --> 02:11:50,159
weather
3966
02:11:50,159 --> 02:11:53,040
and make staffing schedules well we
3967
02:11:53,040 --> 02:11:54,639
already know that wouldn't have solved
3968
02:11:54,639 --> 02:11:56,239
their problem long term
3969
02:11:56,239 --> 02:11:58,639
data analytics gave them the information
3970
02:11:58,639 --> 02:11:59,280
they needed
3971
02:11:59,280 --> 02:12:01,520
to find the best possible solution to
3972
02:12:01,520 --> 02:12:02,880
their problem
3973
02:12:02,880 --> 02:12:05,840
that's the power of data observation and
3974
02:12:05,840 --> 02:12:06,719
intuition
3975
02:12:06,719 --> 02:12:08,880
are powerful tools in decision making
3976
02:12:08,880 --> 02:12:11,199
but they can only take us so far
3977
02:12:11,199 --> 02:12:13,199
when we make decisions based on just
3978
02:12:13,199 --> 02:12:15,360
observation and gut feelings
3979
02:12:15,360 --> 02:12:18,079
we're only seeing part of the picture
3980
02:12:18,079 --> 02:12:20,480
data helps us see the whole thing
3981
02:12:20,480 --> 02:12:23,040
with data we have a complete picture of
3982
02:12:23,040 --> 02:12:23,840
the problem
3983
02:12:23,840 --> 02:12:26,800
and its causes which lets us find new
3984
02:12:26,800 --> 02:12:28,239
and surprising solutions
3985
02:12:28,239 --> 02:12:29,760
we never would have been able to see
3986
02:12:29,760 --> 02:12:31,920
before data analytics
3987
02:12:31,920 --> 02:12:34,239
helps businesses make better decisions
3988
02:12:34,239 --> 02:12:35,040
and it all
3989
02:12:35,040 --> 02:12:37,280
starts with a business task and the
3990
02:12:37,280 --> 02:12:39,199
question it's trying to answer
3991
02:12:39,199 --> 02:12:40,880
with the skills you'll learn throughout
3992
02:12:40,880 --> 02:12:43,199
this program you'll be able to ask the
3993
02:12:43,199 --> 02:12:44,480
right questions
3994
02:12:44,480 --> 02:12:46,719
plan out the best way to gather and
3995
02:12:46,719 --> 02:12:48,800
analyze data and then present it
3996
02:12:48,800 --> 02:12:49,440
visually
3997
02:12:49,440 --> 02:12:51,679
to arm your team so they can make an
3998
02:12:51,679 --> 02:12:52,480
informed
3999
02:12:52,480 --> 02:12:55,599
data-driven decision and that makes you
4000
02:12:55,599 --> 02:12:58,159
critical to the success of any business
4001
02:12:58,159 --> 02:12:59,520
you work for
4002
02:12:59,520 --> 02:13:02,159
data is a powerful tool and with great
4003
02:13:02,159 --> 02:13:03,520
power comes
4004
02:13:03,520 --> 02:13:05,760
well you know the rest and you're doing
4005
02:13:05,760 --> 02:13:07,040
a super job
4006
02:13:07,040 --> 02:13:09,520
taking in all of this information up
4007
02:13:09,520 --> 02:13:10,239
next
4008
02:13:10,239 --> 02:13:12,079
we'll talk about your responsibility as
4009
02:13:12,079 --> 02:13:14,000
a data analyst to make sure you're
4010
02:13:14,000 --> 02:13:14,719
gathering
4011
02:13:14,719 --> 02:13:17,119
analyzing and presenting data in a way
4012
02:13:17,119 --> 02:13:17,920
that's fair
4013
02:13:17,920 --> 02:13:20,000
to the people being represented by that
4014
02:13:20,000 --> 02:13:25,750
data
4015
02:13:25,760 --> 02:13:27,440
hi my name is rachel and i'm the
4016
02:13:27,440 --> 02:13:29,119
business systems and analytics lead at
4017
02:13:29,119 --> 02:13:30,000
verily
4018
02:13:30,000 --> 02:13:31,599
there are a lot of different types of
4019
02:13:31,599 --> 02:13:33,840
problems that a data analyst can solve
4020
02:13:33,840 --> 02:13:36,079
i've been lucky enough over my career to
4021
02:13:36,079 --> 02:13:36,880
have
4022
02:13:36,880 --> 02:13:39,520
to have seen a lot of them and to take
4023
02:13:39,520 --> 02:13:41,840
in a lot of very different types of data
4024
02:13:41,840 --> 02:13:43,599
and help turn that into meaningful
4025
02:13:43,599 --> 02:13:45,280
answers i think one of the most
4026
02:13:45,280 --> 02:13:46,800
important things to remember about data
4027
02:13:46,800 --> 02:13:47,520
analytics
4028
02:13:47,520 --> 02:13:50,880
is that data is data i'm a finance data
4029
02:13:50,880 --> 02:13:51,440
analyst
4030
02:13:51,440 --> 02:13:53,760
and so my role at verily is to take all
4031
02:13:53,760 --> 02:13:55,360
of our financial information
4032
02:13:55,360 --> 02:13:57,599
all of the information of the money
4033
02:13:57,599 --> 02:13:58,800
we're spending and the money we're
4034
02:13:58,800 --> 02:13:59,520
making
4035
02:13:59,520 --> 02:14:02,079
and turn that into reports and insights
4036
02:14:02,079 --> 02:14:03,599
so that our business leads can
4037
02:14:03,599 --> 02:14:05,040
understand what we're doing
4038
02:14:05,040 --> 02:14:06,480
one of the most important things i've
4039
02:14:06,480 --> 02:14:08,719
done it fairly recently was help create
4040
02:14:08,719 --> 02:14:10,159
what's called a profit and loss
4041
02:14:10,159 --> 02:14:12,320
statement for each of our business units
4042
02:14:12,320 --> 02:14:15,199
and that means that in real time our
4043
02:14:15,199 --> 02:14:16,159
teams can see
4044
02:14:16,159 --> 02:14:18,159
what their budget is and how they're
4045
02:14:18,159 --> 02:14:19,840
spending against that budget
4046
02:14:19,840 --> 02:14:21,599
and what that does is that helps our
4047
02:14:21,599 --> 02:14:23,920
teams keep to that budget
4048
02:14:23,920 --> 02:14:26,480
by either increasing their revenue
4049
02:14:26,480 --> 02:14:28,159
streams so that they have more money to
4050
02:14:28,159 --> 02:14:28,960
play with
4051
02:14:28,960 --> 02:14:31,280
or pulling back their spending so that
4052
02:14:31,280 --> 02:14:32,239
they can
4053
02:14:32,239 --> 02:14:34,400
keep themselves within that budget and
4054
02:14:34,400 --> 02:14:36,239
all of that really helps keep us
4055
02:14:36,239 --> 02:14:38,239
on track as a company and making sure
4056
02:14:38,239 --> 02:14:39,599
that we're hitting our goals
4057
02:14:39,599 --> 02:14:42,000
i've found that data acts like a living
4058
02:14:42,000 --> 02:14:43,520
and breathing thing
4059
02:14:43,520 --> 02:14:46,000
when you have a ton of data points it
4060
02:14:46,000 --> 02:14:46,560
can be
4061
02:14:46,560 --> 02:14:48,560
overwhelming when you first sit down to
4062
02:14:48,560 --> 02:14:49,679
make sense of it
4063
02:14:49,679 --> 02:14:52,400
you have tons of columns tons of records
4064
02:14:52,400 --> 02:14:54,800
tons of different types of data
4065
02:14:54,800 --> 02:14:56,719
and finding a way to make sense of that
4066
02:14:56,719 --> 02:14:58,320
is really hard
4067
02:14:58,320 --> 02:14:59,920
and that's where the expertise of a data
4068
02:14:59,920 --> 02:15:02,079
analyst comes in it has been some of the
4069
02:15:02,079 --> 02:15:04,320
most frustrating moments of my career
4070
02:15:04,320 --> 02:15:06,239
but also some of the most rewarding work
4071
02:15:06,239 --> 02:15:07,840
i've ever done when it finally comes
4072
02:15:07,840 --> 02:15:08,719
together
4073
02:15:08,719 --> 02:15:10,480
the best advice i have for any data
4074
02:15:10,480 --> 02:15:12,320
analyst starting out is keep at it
4075
02:15:12,320 --> 02:15:14,159
if the angle you're taking doesn't work
4076
02:15:14,159 --> 02:15:15,760
try to find another one try to come at
4077
02:15:15,760 --> 02:15:16,560
it in a different way
4078
02:15:16,560 --> 02:15:18,320
try to ask a different question
4079
02:15:18,320 --> 02:15:20,079
eventually the data will yield
4080
02:15:20,079 --> 02:15:21,360
and you'll get the insights you're
4081
02:15:21,360 --> 02:15:25,189
looking for
4082
02:15:25,199 --> 02:15:28,000
so far we've covered the different roles
4083
02:15:28,000 --> 02:15:29,360
data analysts play
4084
02:15:29,360 --> 02:15:31,360
in business environments and the kinds
4085
02:15:31,360 --> 02:15:33,520
of tasks that come with those roles
4086
02:15:33,520 --> 02:15:35,440
but data analysts have another important
4087
02:15:35,440 --> 02:15:36,880
responsibility
4088
02:15:36,880 --> 02:15:40,480
making sure their analyses are fair now
4089
02:15:40,480 --> 02:15:42,560
i know what you're probably thinking
4090
02:15:42,560 --> 02:15:44,480
data is based on collected facts
4091
02:15:44,480 --> 02:15:47,520
how can it be unfair well that's a good
4092
02:15:47,520 --> 02:15:48,560
question
4093
02:15:48,560 --> 02:15:50,320
so let's learn what fairness means when
4094
02:15:50,320 --> 02:15:52,000
we talk about data analysis
4095
02:15:52,000 --> 02:15:54,000
and why it's important for you as an
4096
02:15:54,000 --> 02:15:56,159
analyst to keep in mind
4097
02:15:56,159 --> 02:15:57,599
fairness means ensuring that your
4098
02:15:57,599 --> 02:16:00,079
analysis doesn't create or reinforce
4099
02:16:00,079 --> 02:16:03,199
bias in other words as a data analyst
4100
02:16:03,199 --> 02:16:05,280
you want to help create systems that are
4101
02:16:05,280 --> 02:16:06,719
fair and inclusive
4102
02:16:06,719 --> 02:16:09,760
to everyone sounds simple enough
4103
02:16:09,760 --> 02:16:11,840
well here's the tough part about
4104
02:16:11,840 --> 02:16:13,760
fairness and data analytics
4105
02:16:13,760 --> 02:16:15,920
there isn't one standard definition of
4106
02:16:15,920 --> 02:16:17,679
it but hopefully the way
4107
02:16:17,679 --> 02:16:20,000
we've just described it can give you one
4108
02:16:20,000 --> 02:16:21,599
way to think about fairness
4109
02:16:21,599 --> 02:16:23,760
for right now but it's about to get a
4110
02:16:23,760 --> 02:16:25,119
bit trickier
4111
02:16:25,119 --> 02:16:27,199
sometimes conclusions based on data can
4112
02:16:27,199 --> 02:16:29,520
be true and unfair
4113
02:16:29,520 --> 02:16:32,319
what can you do then well let's find out
4114
02:16:32,319 --> 02:16:33,439
with an example
4115
02:16:33,439 --> 02:16:34,880
let's say we have a company that's
4116
02:16:34,880 --> 02:16:38,319
notorious for being kind of a boys club
4117
02:16:38,319 --> 02:16:40,399
it's very male dominated and there
4118
02:16:40,399 --> 02:16:42,639
aren't many women employees
4119
02:16:42,639 --> 02:16:44,319
this company wants to see which
4120
02:16:44,319 --> 02:16:46,719
employees are doing well so they start
4121
02:16:46,719 --> 02:16:48,800
gathering data on employee performance
4122
02:16:48,800 --> 02:16:50,559
and their own company culture
4123
02:16:50,559 --> 02:16:52,719
the data shows that women just aren't
4124
02:16:52,719 --> 02:16:54,240
succeeding as often as men in their
4125
02:16:54,240 --> 02:16:55,200
company
4126
02:16:55,200 --> 02:16:57,760
their conclusion that they should hire
4127
02:16:57,760 --> 02:16:59,120
fewer women
4128
02:16:59,120 --> 02:17:01,519
after all women are doing poorly here
4129
02:17:01,519 --> 02:17:02,319
right
4130
02:17:02,319 --> 02:17:04,880
but that's not a fair conclusion for a
4131
02:17:04,880 --> 02:17:06,479
couple of reasons
4132
02:17:06,479 --> 02:17:08,880
first it doesn't even consider all of
4133
02:17:08,880 --> 02:17:10,080
the available data
4134
02:17:10,080 --> 02:17:12,559
on company culture so it paints an
4135
02:17:12,559 --> 02:17:14,160
incomplete picture
4136
02:17:14,160 --> 02:17:16,240
second it doesn't think about the other
4137
02:17:16,240 --> 02:17:17,519
surrounding factors
4138
02:17:17,519 --> 02:17:20,080
that impact the data or in other words
4139
02:17:20,080 --> 02:17:21,920
the conclusion doesn't consider the
4140
02:17:21,920 --> 02:17:22,880
difficulties
4141
02:17:22,880 --> 02:17:25,679
women have trying to navigate a toxic
4142
02:17:25,679 --> 02:17:27,280
work environment
4143
02:17:27,280 --> 02:17:29,200
if the company only looks at this
4144
02:17:29,200 --> 02:17:31,200
conclusion they won't acknowledge
4145
02:17:31,200 --> 02:17:33,920
and address how harmful their culture is
4146
02:17:33,920 --> 02:17:34,639
and
4147
02:17:34,639 --> 02:17:36,880
they won't understand why women are set
4148
02:17:36,880 --> 02:17:38,399
up to fail within it
4149
02:17:38,399 --> 02:17:40,319
that's why it's important to keep
4150
02:17:40,319 --> 02:17:43,280
fairness in mind when analyzing data
4151
02:17:43,280 --> 02:17:44,639
the conclusion that women aren't
4152
02:17:44,639 --> 02:17:47,120
succeeding in this company is true
4153
02:17:47,120 --> 02:17:49,920
but it ignores the other systemic
4154
02:17:49,920 --> 02:17:50,559
factors
4155
02:17:50,559 --> 02:17:52,800
that are contributing to this problem
4156
02:17:52,800 --> 02:17:53,840
but don't worry
4157
02:17:53,840 --> 02:17:55,519
there's a way to make a fair conclusion
4158
02:17:55,519 --> 02:17:57,679
here an ethical data analyst
4159
02:17:57,679 --> 02:17:59,439
could look at the data gathered and
4160
02:17:59,439 --> 02:18:01,599
conclude that the company culture is
4161
02:18:01,599 --> 02:18:03,359
preventing women from succeeding
4162
02:18:03,359 --> 02:18:05,200
and the company needs to address these
4163
02:18:05,200 --> 02:18:07,760
problems to boost performance
4164
02:18:07,760 --> 02:18:09,599
see how this conclusion paints a much
4165
02:18:09,599 --> 02:18:10,880
more complete and
4166
02:18:10,880 --> 02:18:14,399
fair picture it recognizes the fact
4167
02:18:14,399 --> 02:18:16,479
that women aren't doing as well in this
4168
02:18:16,479 --> 02:18:17,439
company
4169
02:18:17,439 --> 02:18:20,880
and factors in why that could be
4170
02:18:20,880 --> 02:18:22,639
instead of discriminating against women
4171
02:18:22,639 --> 02:18:24,559
applicants in the future
4172
02:18:24,559 --> 02:18:26,240
as a data analyst it's your
4173
02:18:26,240 --> 02:18:28,319
responsibility to make sure your
4174
02:18:28,319 --> 02:18:29,840
analysis is fair
4175
02:18:29,840 --> 02:18:32,240
and factors in the complicated social
4176
02:18:32,240 --> 02:18:33,200
context
4177
02:18:33,200 --> 02:18:35,040
that could create bias in your
4178
02:18:35,040 --> 02:18:36,479
conclusions
4179
02:18:36,479 --> 02:18:38,479
it's important to think about fairness
4180
02:18:38,479 --> 02:18:40,160
from the moment you start collecting
4181
02:18:40,160 --> 02:18:40,639
data
4182
02:18:40,639 --> 02:18:43,040
for a business task to the time you
4183
02:18:43,040 --> 02:18:44,479
present your conclusions
4184
02:18:44,479 --> 02:18:46,559
to your stakeholders we'll learn more
4185
02:18:46,559 --> 02:18:47,519
about bias
4186
02:18:47,519 --> 02:18:50,319
in the data analysis process later on in
4187
02:18:50,319 --> 02:18:51,679
another course
4188
02:18:51,679 --> 02:18:54,399
for now let's check out an example of a
4189
02:18:54,399 --> 02:18:55,679
data analysis
4190
02:18:55,679 --> 02:18:57,920
that does a good job of considering
4191
02:18:57,920 --> 02:19:00,479
fairness in its conclusion
4192
02:19:00,479 --> 02:19:02,559
a team of harvard data scientists were
4193
02:19:02,559 --> 02:19:04,080
developing a mobile platform
4194
02:19:04,080 --> 02:19:05,519
to track patients at risk of
4195
02:19:05,519 --> 02:19:07,359
cardiovascular disease in an
4196
02:19:07,359 --> 02:19:09,359
area of the united states called the
4197
02:19:09,359 --> 02:19:10,399
stroke belt
4198
02:19:10,399 --> 02:19:12,240
it's important to call out that there
4199
02:19:12,240 --> 02:19:13,920
were a variety of reasons
4200
02:19:13,920 --> 02:19:16,240
people living in this area might be more
4201
02:19:16,240 --> 02:19:16,960
at risk
4202
02:19:16,960 --> 02:19:19,120
with that in mind these data scientists
4203
02:19:19,120 --> 02:19:20,240
recognized that
4204
02:19:20,240 --> 02:19:23,280
fairness needed to be a priority for
4205
02:19:23,280 --> 02:19:24,160
this project
4206
02:19:24,160 --> 02:19:26,639
so they built fairness into their models
4207
02:19:26,639 --> 02:19:27,519
the team took
4208
02:19:27,519 --> 02:19:29,359
several fairness measures to make sure
4209
02:19:29,359 --> 02:19:31,040
they were being as fair
4210
02:19:31,040 --> 02:19:33,840
as possible when examining sensitive and
4211
02:19:33,840 --> 02:19:34,639
potentially
4212
02:19:34,639 --> 02:19:37,920
biased data first they teamed analysts
4213
02:19:37,920 --> 02:19:40,240
with social scientists who could provide
4214
02:19:40,240 --> 02:19:42,080
insights on human bias
4215
02:19:42,080 --> 02:19:45,359
and the social context that created them
4216
02:19:45,359 --> 02:19:47,679
they also collected self-reported data
4217
02:19:47,679 --> 02:19:49,120
in a separate system
4218
02:19:49,120 --> 02:19:51,760
to avoid the potential for racial bias
4219
02:19:51,760 --> 02:19:53,680
which might skew the results of their
4220
02:19:53,680 --> 02:19:54,240
study
4221
02:19:54,240 --> 02:19:56,720
and unfairly represent patients and to
4222
02:19:56,720 --> 02:19:57,280
make sure
4223
02:19:57,280 --> 02:19:58,720
their sample population was
4224
02:19:58,720 --> 02:20:01,120
representative they oversampled
4225
02:20:01,120 --> 02:20:03,359
non-dominant groups to ensure their
4226
02:20:03,359 --> 02:20:05,359
motto was including them
4227
02:20:05,359 --> 02:20:07,439
it's clear that the team made fairness a
4228
02:20:07,439 --> 02:20:10,080
top priority every step of the way
4229
02:20:10,080 --> 02:20:11,920
this helped them collect data and create
4230
02:20:11,920 --> 02:20:13,920
conclusions that didn't negatively
4231
02:20:13,920 --> 02:20:15,680
impact the communities they were
4232
02:20:15,680 --> 02:20:17,920
studying hopefully these examples have
4233
02:20:17,920 --> 02:20:20,160
given you a better idea of what fairness
4234
02:20:20,160 --> 02:20:20,640
means
4235
02:20:20,640 --> 02:20:23,200
in data analysis but we're going to keep
4236
02:20:23,200 --> 02:20:24,560
building on your understanding of
4237
02:20:24,560 --> 02:20:25,200
fairness
4238
02:20:25,200 --> 02:20:27,359
throughout this program and you'll get
4239
02:20:27,359 --> 02:20:34,469
to practice with some activities
4240
02:20:34,479 --> 02:20:36,640
hi i'm alex i'm a research scientist at
4241
02:20:36,640 --> 02:20:37,520
google
4242
02:20:37,520 --> 02:20:40,319
my team is called the ethical ai team
4243
02:20:40,319 --> 02:20:41,680
and we're a group of folks that really
4244
02:20:41,680 --> 02:20:43,680
are concerned not only about
4245
02:20:43,680 --> 02:20:45,920
how ai and the technology operates but
4246
02:20:45,920 --> 02:20:48,399
how it interacts with society
4247
02:20:48,399 --> 02:20:51,920
and how it might help or harm
4248
02:20:51,920 --> 02:20:54,080
marginalized communities so when we talk
4249
02:20:54,080 --> 02:20:56,160
about data ethics we think about
4250
02:20:56,160 --> 02:20:58,399
you know what is the good and right way
4251
02:20:58,399 --> 02:21:00,160
of using data
4252
02:21:00,160 --> 02:21:01,840
what are going to be ways that are going
4253
02:21:01,840 --> 02:21:03,280
to be uses of data that are going to be
4254
02:21:03,280 --> 02:21:04,560
beneficial to people
4255
02:21:04,560 --> 02:21:06,160
when it comes to data ethics it's not
4256
02:21:06,160 --> 02:21:08,319
just about minimizing harm but it's
4257
02:21:08,319 --> 02:21:08,960
actually this
4258
02:21:08,960 --> 02:21:11,600
this concept of beneficence how do we
4259
02:21:11,600 --> 02:21:12,000
actually
4260
02:21:12,000 --> 02:21:14,080
improve the lives of people by using
4261
02:21:14,080 --> 02:21:15,359
data
4262
02:21:15,359 --> 02:21:17,520
when we think about data ethics we're
4263
02:21:17,520 --> 02:21:18,640
thinking about
4264
02:21:18,640 --> 02:21:20,560
who's collecting the data why are they
4265
02:21:20,560 --> 02:21:22,479
collecting it how are they collecting it
4266
02:21:22,479 --> 02:21:23,920
and what for what purpose
4267
02:21:23,920 --> 02:21:26,319
because of the way that organizations
4268
02:21:26,319 --> 02:21:28,479
have imperatives to
4269
02:21:28,479 --> 02:21:31,680
make money or to report to somebody or
4270
02:21:31,680 --> 02:21:34,399
provide some kind of analysis we also
4271
02:21:34,399 --> 02:21:35,359
have to keep
4272
02:21:35,359 --> 02:21:37,280
strongly in mind how this is actually
4273
02:21:37,280 --> 02:21:38,399
going to benefit people
4274
02:21:38,399 --> 02:21:40,720
at the end of the day are the people
4275
02:21:40,720 --> 02:21:42,319
represented in this data
4276
02:21:42,319 --> 02:21:44,399
going to be benefited by this and i
4277
02:21:44,399 --> 02:21:46,240
think that's the thing
4278
02:21:46,240 --> 02:21:47,840
you never want to lose sight of as a
4279
02:21:47,840 --> 02:21:49,920
data scientist or a data analyst
4280
02:21:49,920 --> 02:21:51,760
i think aspiring data analysts need to
4281
02:21:51,760 --> 02:21:53,040
keep in mind that
4282
02:21:53,040 --> 02:21:54,880
a lot of the data that you're going to
4283
02:21:54,880 --> 02:21:57,680
encounter is data that comes from people
4284
02:21:57,680 --> 02:22:01,200
so at the end of the day data are people
4285
02:22:01,200 --> 02:22:05,439
and you want to have a responsibility
4286
02:22:05,439 --> 02:22:08,800
to those people that are represented in
4287
02:22:08,800 --> 02:22:12,160
those data second is thinking about
4288
02:22:12,160 --> 02:22:14,399
how to keep aspects of their data
4289
02:22:14,399 --> 02:22:16,479
protected and private
4290
02:22:16,479 --> 02:22:20,080
we don't want to go through our practice
4291
02:22:20,080 --> 02:22:22,399
thinking about data instances as
4292
02:22:22,399 --> 02:22:23,760
something we could just
4293
02:22:23,760 --> 02:22:25,840
throw on the web no there needs to be
4294
02:22:25,840 --> 02:22:28,000
considerations about how to keep
4295
02:22:28,000 --> 02:22:30,399
that information and likenesses like
4296
02:22:30,399 --> 02:22:33,280
their images or their voices or their
4297
02:22:33,280 --> 02:22:35,520
or their text how do we keep that
4298
02:22:35,520 --> 02:22:36,960
private we also
4299
02:22:36,960 --> 02:22:40,160
need to think about how
4300
02:22:40,160 --> 02:22:43,439
we can have mechanisms of giving users
4301
02:22:43,439 --> 02:22:45,520
and giving consumers more control
4302
02:22:45,520 --> 02:22:48,160
over their data it's not going to be
4303
02:22:48,160 --> 02:22:49,600
sufficient just to say
4304
02:22:49,600 --> 02:22:52,399
we collect elect all this data and trust
4305
02:22:52,399 --> 02:22:53,040
us
4306
02:22:53,040 --> 02:22:56,399
uh with all these data but we need to
4307
02:22:56,399 --> 02:22:57,200
ensure that
4308
02:22:57,200 --> 02:22:59,840
there's actionable ways in which people
4309
02:22:59,840 --> 02:23:01,920
can consent to giving those data
4310
02:23:01,920 --> 02:23:04,479
and ways that they can ask for it to be
4311
02:23:04,479 --> 02:23:06,640
revoked or removed
4312
02:23:06,640 --> 02:23:09,760
so data is growing and at the same time
4313
02:23:09,760 --> 02:23:11,200
we need to empower people to have
4314
02:23:11,200 --> 02:23:12,960
control over their own data
4315
02:23:12,960 --> 02:23:16,000
the future is that data is always
4316
02:23:16,000 --> 02:23:17,680
growing we haven't seen any kind of
4317
02:23:17,680 --> 02:23:20,319
evidence that data is actually shrinking
4318
02:23:20,319 --> 02:23:22,399
and with the knowledge that data is
4319
02:23:22,399 --> 02:23:23,359
growing these
4320
02:23:23,359 --> 02:23:26,240
issues become more and more peaked and
4321
02:23:26,240 --> 02:23:27,200
more and more
4322
02:23:27,200 --> 02:23:32,080
important to think about
4323
02:23:32,080 --> 02:23:34,399
now we know that there are all kinds of
4324
02:23:34,399 --> 02:23:36,000
jobs in different industries
4325
02:23:36,000 --> 02:23:38,960
available for data analysts but now it's
4326
02:23:38,960 --> 02:23:40,880
time to think about something just as
4327
02:23:40,880 --> 02:23:41,680
important
4328
02:23:41,680 --> 02:23:44,080
how can you tell if a job is a good fit
4329
02:23:44,080 --> 02:23:44,960
for you
4330
02:23:44,960 --> 02:23:48,080
and your career goals tough one right
4331
02:23:48,080 --> 02:23:49,920
don't worry that's exactly what we'll
4332
02:23:49,920 --> 02:23:51,359
cover in this video
4333
02:23:51,359 --> 02:23:53,200
there's a lot of important factors to
4334
02:23:53,200 --> 02:23:54,720
think about when searching for your
4335
02:23:54,720 --> 02:23:55,600
dream job
4336
02:23:55,600 --> 02:23:57,359
let's talk about some of the most common
4337
02:23:57,359 --> 02:23:58,960
factors first
4338
02:23:58,960 --> 02:24:02,000
industry tools location
4339
02:24:02,000 --> 02:24:05,280
travel and culture data is already being
4340
02:24:05,280 --> 02:24:06,960
used by countless industries
4341
02:24:06,960 --> 02:24:09,760
in all kinds of different ways tech
4342
02:24:09,760 --> 02:24:10,640
marketing
4343
02:24:10,640 --> 02:24:14,319
finance health care the list goes on
4344
02:24:14,319 --> 02:24:16,160
but one thing that's important to keep
4345
02:24:16,160 --> 02:24:19,200
in mind every industry has specific data
4346
02:24:19,200 --> 02:24:19,840
needs
4347
02:24:19,840 --> 02:24:22,000
that have to be addressed differently by
4348
02:24:22,000 --> 02:24:23,439
their data analysts
4349
02:24:23,439 --> 02:24:25,359
the same revenue data can be used in
4350
02:24:25,359 --> 02:24:26,479
three different ways
4351
02:24:26,479 --> 02:24:28,479
by data analysts in three different
4352
02:24:28,479 --> 02:24:30,720
industries financial services
4353
02:24:30,720 --> 02:24:33,840
telecom and tech for example
4354
02:24:33,840 --> 02:24:36,319
a finance analyst at a bank post public
4355
02:24:36,319 --> 02:24:38,960
revenue data of telecom company x
4356
02:24:38,960 --> 02:24:41,520
to create a forecast that predicts where
4357
02:24:41,520 --> 02:24:43,200
revenues will be in the future
4358
02:24:43,200 --> 02:24:44,880
to recommend the stock price the
4359
02:24:44,880 --> 02:24:47,680
business analyst at telecom company x
4360
02:24:47,680 --> 02:24:50,479
uses that same data to advise the sales
4361
02:24:50,479 --> 02:24:51,520
team
4362
02:24:51,520 --> 02:24:54,080
then a data analyst at the company who
4363
02:24:54,080 --> 02:24:56,160
created a customer management tool
4364
02:24:56,160 --> 02:24:58,960
for telecom company x will use that
4365
02:24:58,960 --> 02:24:59,920
revenue data
4366
02:24:59,920 --> 02:25:01,920
to determine how efficiently their
4367
02:25:01,920 --> 02:25:03,760
software is performing
4368
02:25:03,760 --> 02:25:06,800
finance telecom and tech
4369
02:25:06,800 --> 02:25:09,280
all use data differently so they need
4370
02:25:09,280 --> 02:25:10,080
analysts
4371
02:25:10,080 --> 02:25:12,479
who have different skills it all comes
4372
02:25:12,479 --> 02:25:14,319
down to what the needs of the industry
4373
02:25:14,319 --> 02:25:14,960
are
4374
02:25:14,960 --> 02:25:17,280
those needs will determine what kind of
4375
02:25:17,280 --> 02:25:18,000
task
4376
02:25:18,000 --> 02:25:20,240
you'll be given the questions you'll be
4377
02:25:20,240 --> 02:25:22,319
answering and even how you'll approach
4378
02:25:22,319 --> 02:25:23,439
job searching
4379
02:25:23,439 --> 02:25:25,359
if you're just starting out a great way
4380
02:25:25,359 --> 02:25:26,560
to guide your search
4381
02:25:26,560 --> 02:25:28,640
is to think first about what you're
4382
02:25:28,640 --> 02:25:29,920
interested in
4383
02:25:29,920 --> 02:25:32,160
does helping people get healthier sound
4384
02:25:32,160 --> 02:25:33,280
meaningful to you
4385
02:25:33,280 --> 02:25:35,439
maybe you want to focus on using data to
4386
02:25:35,439 --> 02:25:37,040
improve hospital admissions
4387
02:25:37,040 --> 02:25:39,120
what about helping people save for a
4388
02:25:39,120 --> 02:25:40,880
happy retirement
4389
02:25:40,880 --> 02:25:43,120
you might want a job that uses data to
4390
02:25:43,120 --> 02:25:44,880
determine risk factors and financial
4391
02:25:44,880 --> 02:25:46,080
investments
4392
02:25:46,080 --> 02:25:47,680
or maybe you're interested in helping
4393
02:25:47,680 --> 02:25:50,000
journalism grow in your city
4394
02:25:50,000 --> 02:25:52,560
a job using data to help find your local
4395
02:25:52,560 --> 02:25:53,680
news website
4396
02:25:53,680 --> 02:25:55,760
find more subscribers could be the
4397
02:25:55,760 --> 02:25:57,760
perfect role for you
4398
02:25:57,760 --> 02:26:00,240
the key is to think about your interest
4399
02:26:00,240 --> 02:26:02,720
early in your job search
4400
02:26:02,720 --> 02:26:03,840
that will lead you in the right
4401
02:26:03,840 --> 02:26:05,920
direction and it it'll help you in
4402
02:26:05,920 --> 02:26:07,520
interviews too
4403
02:26:07,520 --> 02:26:09,359
potential employers will want to know
4404
02:26:09,359 --> 02:26:11,439
why you're interested in their company
4405
02:26:11,439 --> 02:26:13,920
and how you can address their needs so
4406
02:26:13,920 --> 02:26:16,000
if you can speak about your motivation
4407
02:26:16,000 --> 02:26:17,600
to work in data analytics during
4408
02:26:17,600 --> 02:26:19,600
interviews you'll make yourself stand
4409
02:26:19,600 --> 02:26:21,200
out in a great way
4410
02:26:21,200 --> 02:26:23,439
you'll have options when it comes to
4411
02:26:23,439 --> 02:26:24,640
where you work
4412
02:26:24,640 --> 02:26:27,920
and who you work for but remember
4413
02:26:27,920 --> 02:26:30,720
you want to enjoy what you do so it's a
4414
02:26:30,720 --> 02:26:32,160
good idea to think about
4415
02:26:32,160 --> 02:26:34,319
how you want to use your skills then
4416
02:26:34,319 --> 02:26:35,359
search for jobs
4417
02:26:35,359 --> 02:26:37,840
that allow you to do that next on the
4418
02:26:37,840 --> 02:26:39,680
list of things to think about
4419
02:26:39,680 --> 02:26:42,160
location and travel when you start your
4420
02:26:42,160 --> 02:26:43,040
job search
4421
02:26:43,040 --> 02:26:44,560
you need to make some decisions about
4422
02:26:44,560 --> 02:26:46,960
where you want to live so it helps to
4423
02:26:46,960 --> 02:26:48,640
ask yourself some questions
4424
02:26:48,640 --> 02:26:50,240
does your preferred industry have
4425
02:26:50,240 --> 02:26:52,640
opportunities in your area
4426
02:26:52,640 --> 02:26:54,399
are you trying to stay local or would
4427
02:26:54,399 --> 02:26:56,560
you be happy relocating
4428
02:26:56,560 --> 02:26:58,479
how long are you willing to commute to
4429
02:26:58,479 --> 02:26:59,840
work every day
4430
02:26:59,840 --> 02:27:02,960
will you drive to work walk take public
4431
02:27:02,960 --> 02:27:04,240
transport
4432
02:27:04,240 --> 02:27:06,800
is that possible year round how do you
4433
02:27:06,800 --> 02:27:08,720
feel about working remotely
4434
02:27:08,720 --> 02:27:10,880
does working from home excite you or
4435
02:27:10,880 --> 02:27:11,920
bore you
4436
02:27:11,920 --> 02:27:13,920
and of course you'll want to consider
4437
02:27:13,920 --> 02:27:14,960
cost of living
4438
02:27:14,960 --> 02:27:16,960
and whether or not you want the
4439
02:27:16,960 --> 02:27:18,960
convenience of city living or a quiet
4440
02:27:18,960 --> 02:27:20,080
suburban home
4441
02:27:20,080 --> 02:27:21,600
and it's not just about where you'll be
4442
02:27:21,600 --> 02:27:24,240
based some jobs may ask you to travel
4443
02:27:24,240 --> 02:27:26,000
which could be an exciting chance to see
4444
02:27:26,000 --> 02:27:28,479
the world or a deal breaker
4445
02:27:28,479 --> 02:27:30,319
it's all about what you want out of this
4446
02:27:30,319 --> 02:27:32,560
job so start asking yourself some of
4447
02:27:32,560 --> 02:27:33,840
these questions
4448
02:27:33,840 --> 02:27:35,520
figuring out the answers can help you
4449
02:27:35,520 --> 02:27:37,760
narrow down your search even further
4450
02:27:37,760 --> 02:27:40,000
so you're only looking at jobs you'd
4451
02:27:40,000 --> 02:27:41,520
actually accept
4452
02:27:41,520 --> 02:27:43,920
once you've answered enough questions
4453
02:27:43,920 --> 02:27:45,840
you'll be able to identify some specific
4454
02:27:45,840 --> 02:27:46,640
companies
4455
02:27:46,640 --> 02:27:49,439
that fit your needs at this point it's a
4456
02:27:49,439 --> 02:27:51,680
good time to think about your values
4457
02:27:51,680 --> 02:27:53,840
and what kind of company culture is a
4458
02:27:53,840 --> 02:27:55,680
good fit for you
4459
02:27:55,680 --> 02:27:58,880
ready here comes some more questions
4460
02:27:58,880 --> 02:28:00,960
do you work best in a team or by
4461
02:28:00,960 --> 02:28:02,240
yourself
4462
02:28:02,240 --> 02:28:04,800
do you like to have a set routine or do
4463
02:28:04,800 --> 02:28:05,520
you enjoy
4464
02:28:05,520 --> 02:28:07,600
taking a new project and trying new
4465
02:28:07,600 --> 02:28:08,800
things
4466
02:28:08,800 --> 02:28:10,479
do your values match the company's
4467
02:28:10,479 --> 02:28:12,880
values you'll want to pay attention to
4468
02:28:12,880 --> 02:28:14,880
these things during your job search
4469
02:28:14,880 --> 02:28:17,280
and interview process so you can be sure
4470
02:28:17,280 --> 02:28:18,000
you fully
4471
02:28:18,000 --> 02:28:20,720
invested in the company you work for
4472
02:28:20,720 --> 02:28:22,479
that's the best way to start building an
4473
02:28:22,479 --> 02:28:23,200
exciting
4474
02:28:23,200 --> 02:28:26,000
and fulfilling career this program will
4475
02:28:26,000 --> 02:28:28,160
help you learn the core skills for data
4476
02:28:28,160 --> 02:28:28,960
analytics in
4477
02:28:28,960 --> 02:28:31,359
any setting it's up to you where you
4478
02:28:31,359 --> 02:28:33,040
want to take them
4479
02:28:33,040 --> 02:28:34,720
whether that means starting in a
4480
02:28:34,720 --> 02:28:36,319
completely new industry
4481
02:28:36,319 --> 02:28:38,640
or moving into an analyst position in an
4482
02:28:38,640 --> 02:28:41,600
industry you already have experience in
4483
02:28:41,600 --> 02:28:43,520
and hopefully what we've covered here
4484
02:28:43,520 --> 02:28:45,600
has helped you get on track for your
4485
02:28:45,600 --> 02:28:46,479
future job
4486
02:28:46,479 --> 02:28:49,120
search after this you'll have a few
4487
02:28:49,120 --> 02:28:50,319
activities to do
4488
02:28:50,319 --> 02:28:51,760
and then you'll be able to move on to
4489
02:28:51,760 --> 02:28:53,600
the next part of this course
4490
02:28:53,600 --> 02:28:56,319
we learned a lot so far like what kinds
4491
02:28:56,319 --> 02:28:58,160
of opportunities are out there for data
4492
02:28:58,160 --> 02:28:58,800
analysts
4493
02:28:58,800 --> 02:29:00,800
in different industries how data
4494
02:29:00,800 --> 02:29:02,399
analysts help businesses
4495
02:29:02,399 --> 02:29:04,880
make better decisions the importance of
4496
02:29:04,880 --> 02:29:06,720
fairness and data analytics
4497
02:29:06,720 --> 02:29:08,720
and the potential questions you can
4498
02:29:08,720 --> 02:29:10,080
start asking yourself
4499
02:29:10,080 --> 02:29:12,399
before your future job search and you
4500
02:29:12,399 --> 02:29:14,720
can always look back at these lessons
4501
02:29:14,720 --> 02:29:17,040
if you want to review in an upcoming
4502
02:29:17,040 --> 02:29:17,760
course
4503
02:29:17,760 --> 02:29:19,680
we'll look at the skills all successful
4504
02:29:19,680 --> 02:29:21,040
data analysts have
4505
02:29:21,040 --> 02:29:22,479
and you'll learn how you can start
4506
02:29:22,479 --> 02:29:24,080
practicing them too
4507
02:29:24,080 --> 02:29:25,840
but before that you'll have an
4508
02:29:25,840 --> 02:29:27,760
assessment good luck
4509
02:29:27,760 --> 02:29:34,469
and i'll see you later
4510
02:29:34,479 --> 02:29:37,120
my name is sama moid and i'm a recruiter
4511
02:29:37,120 --> 02:29:39,120
here at google for the large customer
4512
02:29:39,120 --> 02:29:39,920
sales team
4513
02:29:39,920 --> 02:29:41,760
basically i hire talent for the sales
4514
02:29:41,760 --> 02:29:43,359
team here even within the sales
4515
02:29:43,359 --> 02:29:44,160
recruiting space
4516
02:29:44,160 --> 02:29:45,600
i recruit specifically for the
4517
02:29:45,600 --> 02:29:47,280
analytical lead
4518
02:29:47,280 --> 02:29:49,120
roles here at google i want the
4519
02:29:49,120 --> 02:29:50,399
candidate to be as comfortable as
4520
02:29:50,399 --> 02:29:51,439
possible
4521
02:29:51,439 --> 02:29:54,000
as a recruiter i'm also their advocate
4522
02:29:54,000 --> 02:29:55,439
if they're a good fit for the team i'd
4523
02:29:55,439 --> 02:29:56,960
like to present them in the best light
4524
02:29:56,960 --> 02:29:59,680
as a recruiter some advice i would give
4525
02:29:59,680 --> 02:30:00,080
for
4526
02:30:00,080 --> 02:30:02,080
a data analyst that's just starting to
4527
02:30:02,080 --> 02:30:03,520
look for a job
4528
02:30:03,520 --> 02:30:04,960
think about a time where you've used
4529
02:30:04,960 --> 02:30:06,800
data to solve a problem whether it's in
4530
02:30:06,800 --> 02:30:07,600
your professional
4531
02:30:07,600 --> 02:30:10,319
or personal projects another tip i would
4532
02:30:10,319 --> 02:30:11,040
say for
4533
02:30:11,040 --> 02:30:13,040
a data analyst that's just looking for a
4534
02:30:13,040 --> 02:30:15,040
new a new job is to increase your
4535
02:30:15,040 --> 02:30:16,560
professional network
4536
02:30:16,560 --> 02:30:17,840
there are many ways to increase your
4537
02:30:17,840 --> 02:30:20,479
professional network one of them is to
4538
02:30:20,479 --> 02:30:22,160
increase your online footprint reach out
4539
02:30:22,160 --> 02:30:25,120
to other analysts on linkedin
4540
02:30:25,120 --> 02:30:27,040
join local meetups with other data
4541
02:30:27,040 --> 02:30:28,800
scientists sometimes when we're looking
4542
02:30:28,800 --> 02:30:30,000
for a really
4543
02:30:30,000 --> 02:30:32,720
a unique skill set recruiters are going
4544
02:30:32,720 --> 02:30:34,479
on websites like linkedin and github and
4545
02:30:34,479 --> 02:30:36,240
trying to find that talent themselves
4546
02:30:36,240 --> 02:30:37,439
it's really important to have your
4547
02:30:37,439 --> 02:30:39,280
linkedin updated along with
4548
02:30:39,280 --> 02:30:40,960
websites like github where you can
4549
02:30:40,960 --> 02:30:42,560
showcase a lot of the data analyst
4550
02:30:42,560 --> 02:30:44,160
projects you've done
4551
02:30:44,160 --> 02:30:46,319
another tip i would say for an in-person
4552
02:30:46,319 --> 02:30:47,760
interview is to prepare questions for
4553
02:30:47,760 --> 02:30:49,359
the interviewer
4554
02:30:49,359 --> 02:30:51,760
make sure they're not broad questions
4555
02:30:51,760 --> 02:30:53,200
they should be questions that will help
4556
02:30:53,200 --> 02:30:53,520
you
4557
02:30:53,520 --> 02:30:56,160
understand the team and and the job
4558
02:30:56,160 --> 02:30:56,880
better
4559
02:30:56,880 --> 02:30:58,240
if you're given a case study in an
4560
02:30:58,240 --> 02:31:00,080
interview you should expect to be given
4561
02:31:00,080 --> 02:31:01,680
a business problem along with a sample
4562
02:31:01,680 --> 02:31:02,720
data set
4563
02:31:02,720 --> 02:31:04,319
then you'd be asked to take that sample
4564
02:31:04,319 --> 02:31:06,160
data set analyze it
4565
02:31:06,160 --> 02:31:08,080
and come up with a solution one of the
4566
02:31:08,080 --> 02:31:09,520
things you can do to help prepare
4567
02:31:09,520 --> 02:31:10,479
yourself for this
4568
02:31:10,479 --> 02:31:14,800
is to ensure you are analyzing the data
4569
02:31:14,800 --> 02:31:16,080
and coming up with a solution that
4570
02:31:16,080 --> 02:31:17,520
relates back to that data
4571
02:31:17,520 --> 02:31:19,600
sometimes there is no right answer and a
4572
02:31:19,600 --> 02:31:21,280
lot of times interviewers are looking to
4573
02:31:21,280 --> 02:31:22,720
see your thought process and the way you
4574
02:31:22,720 --> 02:31:24,560
get to your solution
4575
02:31:24,560 --> 02:31:26,240
i highly encourage that if you find a
4576
02:31:26,240 --> 02:31:28,000
role that you're interested in not only
4577
02:31:28,000 --> 02:31:29,760
apply to it but go the next step look
4578
02:31:29,760 --> 02:31:31,200
for the recruiter look for the hiring
4579
02:31:31,200 --> 02:31:32,880
manager online see if you can reach out
4580
02:31:32,880 --> 02:31:33,439
to them
4581
02:31:33,439 --> 02:31:35,359
and set up a coffee chat or send them
4582
02:31:35,359 --> 02:31:37,200
your resume directly
4583
02:31:37,200 --> 02:31:39,760
online applications could be a really
4584
02:31:39,760 --> 02:31:40,240
big
4585
02:31:40,240 --> 02:31:42,720
black hole where you never hear back
4586
02:31:42,720 --> 02:31:44,800
from the recruiter or the team
4587
02:31:44,800 --> 02:31:46,319
when you reach out directly to a hiring
4588
02:31:46,319 --> 02:31:48,160
manager or recruiter it really shows
4589
02:31:48,160 --> 02:31:48,560
your
4590
02:31:48,560 --> 02:31:51,280
eagerness uh for the role and your
4591
02:31:51,280 --> 02:31:52,560
interest for the role
4592
02:31:52,560 --> 02:31:54,160
even if sometimes you don't get a
4593
02:31:54,160 --> 02:31:56,000
response from that from reaching out you
4594
02:31:56,000 --> 02:31:57,040
never know it's
4595
02:31:57,040 --> 02:31:58,800
you know you try multiple different
4596
02:31:58,800 --> 02:32:00,160
times and that one time you get a
4597
02:32:00,160 --> 02:32:01,920
response back from a recruiter or hiring
4598
02:32:01,920 --> 02:32:02,479
manager
4599
02:32:02,479 --> 02:32:03,840
could be the time you get the job that
4600
02:32:03,840 --> 02:32:07,830
you really wanted
4601
02:32:07,840 --> 02:32:09,680
we're at the end of this course which
4602
02:32:09,680 --> 02:32:11,359
means it's time to show off what you've
4603
02:32:11,359 --> 02:32:12,399
learned
4604
02:32:12,399 --> 02:32:14,080
we've covered the different kinds of
4605
02:32:14,080 --> 02:32:15,680
industries using data to drive
4606
02:32:15,680 --> 02:32:18,479
decisions and how you can help them how
4607
02:32:18,479 --> 02:32:20,640
to promote fairness in your data work
4608
02:32:20,640 --> 02:32:22,880
and opportunities that are out there in
4609
02:32:22,880 --> 02:32:24,080
the world of data
4610
02:32:24,080 --> 02:32:26,880
analytics i know you've got this and
4611
02:32:26,880 --> 02:32:29,200
once you finish the course challenge
4612
02:32:29,200 --> 02:32:31,040
i'll be right here to introduce you to
4613
02:32:31,040 --> 02:32:33,760
the next course
4614
02:32:33,760 --> 02:32:36,000
congratulations on finishing this first
4615
02:32:36,000 --> 02:32:36,800
course
4616
02:32:36,800 --> 02:32:39,200
you've already learned a lot and you're
4617
02:32:39,200 --> 02:32:40,000
ready to take
4618
02:32:40,000 --> 02:32:42,880
what you've learned and move forward and
4619
02:32:42,880 --> 02:32:44,560
if you ever need a refresher
4620
02:32:44,560 --> 02:32:46,399
just remember that these videos will
4621
02:32:46,399 --> 02:32:49,359
still be here whenever you need them
4622
02:32:49,359 --> 02:32:51,040
you might remember your next instructor
4623
02:32:51,040 --> 02:32:52,720
from her introduction at the beginning
4624
02:32:52,720 --> 02:32:54,160
of the intro course
4625
02:32:54,160 --> 02:32:56,640
get ready to meet my fellow googler and
4626
02:32:56,640 --> 02:32:57,680
your instructor
4627
02:32:57,680 --> 02:33:00,720
for the next course ximena she's ready
4628
02:33:00,720 --> 02:33:02,399
to help you get started on your next
4629
02:33:02,399 --> 02:33:02,960
step
4630
02:33:02,960 --> 02:33:04,720
towards finishing this program and
4631
02:33:04,720 --> 02:33:07,040
becoming a data analyst
4632
02:33:07,040 --> 02:33:09,280
this next course will build directly on
4633
02:33:09,280 --> 02:33:10,319
some of the topics
4634
02:33:10,319 --> 02:33:12,560
that you've learned so far and give you
4635
02:33:12,560 --> 02:33:13,840
insight into the things
4636
02:33:13,840 --> 02:33:16,640
we've already talked about like any good
4637
02:33:16,640 --> 02:33:17,439
detective
4638
02:33:17,439 --> 02:33:19,600
you learn how to ask the right questions
4639
02:33:19,600 --> 02:33:20,800
and use data
4640
02:33:20,800 --> 02:33:24,080
to find answers employees in every
4641
02:33:24,080 --> 02:33:24,880
industry
4642
02:33:24,880 --> 02:33:26,640
need to become comfortable asking
4643
02:33:26,640 --> 02:33:28,960
questions but this can especially be
4644
02:33:28,960 --> 02:33:30,720
true for data analysts
4645
02:33:30,720 --> 02:33:32,720
a lot of data analysts try to make their
4646
02:33:32,720 --> 02:33:34,960
work perfect the first time
4647
02:33:34,960 --> 02:33:36,960
even though they might not have all the
4648
02:33:36,960 --> 02:33:38,399
information
4649
02:33:38,399 --> 02:33:40,560
instead of asking questions they make
4650
02:33:40,560 --> 02:33:41,840
assumptions
4651
02:33:41,840 --> 02:33:44,880
that can lead to mistakes it's so much
4652
02:33:44,880 --> 02:33:48,160
better to be humble and inquisitive
4653
02:33:48,160 --> 02:33:51,439
and to ask questions i'll show you what
4654
02:33:51,439 --> 02:33:52,720
i mean
4655
02:33:52,720 --> 02:33:54,720
one of the analysts i supervised came
4656
02:33:54,720 --> 02:33:57,120
into google with no coding experience
4657
02:33:57,120 --> 02:33:58,720
he was nervous about leaving a great
4658
02:33:58,720 --> 02:34:01,280
first impression so he tried to study up
4659
02:34:01,280 --> 02:34:03,359
on multiple languages by himself
4660
02:34:03,359 --> 02:34:06,000
before he started when the work actually
4661
02:34:06,000 --> 02:34:06,880
began
4662
02:34:06,880 --> 02:34:10,240
he didn't ask us his team questions
4663
02:34:10,240 --> 02:34:12,080
or ask us for help when he ran into
4664
02:34:12,080 --> 02:34:13,840
roadblocks
4665
02:34:13,840 --> 02:34:16,080
there are a lot of great places to find
4666
02:34:16,080 --> 02:34:18,160
answers especially online
4667
02:34:18,160 --> 02:34:20,560
and his initiative helped him find some
4668
02:34:20,560 --> 02:34:22,080
of those places
4669
02:34:22,080 --> 02:34:24,240
but at the end of the day he forgot to
4670
02:34:24,240 --> 02:34:25,359
tap into his best
4671
02:34:25,359 --> 02:34:28,640
resource us his team
4672
02:34:28,640 --> 02:34:30,240
because he was nervous about how he
4673
02:34:30,240 --> 02:34:32,399
would be perceived if he asked us for
4674
02:34:32,399 --> 02:34:33,200
help
4675
02:34:33,200 --> 02:34:34,880
he almost missed out on some great
4676
02:34:34,880 --> 02:34:37,359
insights from his team members
4677
02:34:37,359 --> 02:34:39,920
as roadblocks persisted he realized he
4678
02:34:39,920 --> 02:34:41,840
needed to make a change
4679
02:34:41,840 --> 02:34:44,000
he stopped trying to guess expectations
4680
02:34:44,000 --> 02:34:45,600
processes and more
4681
02:34:45,600 --> 02:34:47,920
all on his own and started asking us
4682
02:34:47,920 --> 02:34:49,600
more questions
4683
02:34:49,600 --> 02:34:51,840
as soon as he embraced this new approach
4684
02:34:51,840 --> 02:34:54,080
he skyrocketed on our team
4685
02:34:54,080 --> 02:34:55,760
his learning went straight up the curve
4686
02:34:55,760 --> 02:34:57,280
like a hockey stick
4687
02:34:57,280 --> 02:34:59,439
his impact on the organization the
4688
02:34:59,439 --> 02:35:01,600
number of people who reach out to him
4689
02:35:01,600 --> 02:35:03,760
and his career path going forward all
4690
02:35:03,760 --> 02:35:05,280
did the same
4691
02:35:05,280 --> 02:35:07,520
the bottom line is you don't need to
4692
02:35:07,520 --> 02:35:09,040
know it all
4693
02:35:09,040 --> 02:35:12,000
the saying is true there are no bad
4694
02:35:12,000 --> 02:35:13,200
questions
4695
02:35:13,200 --> 02:35:14,800
being open to learning is one of the
4696
02:35:14,800 --> 02:35:16,479
most important qualities
4697
02:35:16,479 --> 02:35:20,000
for a data analyst speaking of learning
4698
02:35:20,000 --> 02:35:22,240
in the next course we'll go into more
4699
02:35:22,240 --> 02:35:24,160
depth learning about basic spreadsheet
4700
02:35:24,160 --> 02:35:24,960
skills
4701
02:35:24,960 --> 02:35:27,040
and when you'll need to use them you'll
4702
02:35:27,040 --> 02:35:28,720
discover how to apply structured
4703
02:35:28,720 --> 02:35:30,160
thinking to data work
4704
02:35:30,160 --> 02:35:32,319
and you'll focus on how to best meet
4705
02:35:32,319 --> 02:35:33,439
stakeholder needs
4706
02:35:33,439 --> 02:35:35,680
and expectations by gathering all the
4707
02:35:35,680 --> 02:35:36,880
clues
4708
02:35:36,880 --> 02:35:39,280
great work and good luck on the next
4709
02:35:39,280 --> 02:35:41,840
course310145
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