Original subtitles

[Music]

what do companies in e-commerce

entertainment healthcare manufacturing

marketing finance tech and hundreds of

other industries

all have in common you guessed it they

all

use data organizations of all kinds

need data analysts to help them improve

their processes

identify opportunities and trends launch

new products

provide great customer service and make

thoughtful

decisions hi i'm tony a program manager

at google

and a data analyst myself i'd like to

welcome you to the google data analytics

certificate

now there are lots of great reasons to

earn this certificate

maybe you're thinking about starting a

career in the exciting world of data

analytics

or maybe you're just fascinated by the

power of data as i

am no matter what brought you here

you're in the right place to kick-start

a career

and learn industry relevant skills in

data analytics

but first what exactly is data

well i like to say that data is a

collection of facts

this collection can include numbers

pictures

videos words measurements observations

and more

once you have data analytics puts it to

work through analysis

data analysis is the collection

transformation

and organization of data in order to

draw conclusions

make predictions and drive informed

decision making

and it doesn't stop there data evolves

over time

which means this analysis or analytics

as we call

it can give us new information

throughout data's entire

life cycle data is everywhere

you use and create data every day have

you ever read reviews of a product

before deciding whether or not to buy it

that's data analysis

or maybe you wear a fitness tracker to

count your steps

so you can stay active throughout the

day that's data analysis

but you don't just use data you also

create huge amounts of it

every single day anytime you use your

phone

look up something online stream music

shop with the credit card post on social

media

or use gps to map a route you're

creating data

our digital world and the millions of

smart devices inside of it

have made the amount of data available

truly mind-blowing

here at google we process more than

forty 000 searches

every second that's 3.5 billion searches

a day

and 1.2 trillion searches every year

here's another way to think about it

youtube has almost

2 billion users if youtube users

made up a country it would be the

largest in the world

all of that data is transforming the

world around us

the publication the economist recently

called data

the world's most valuable resource so

it's easy to see why data analysts

are so valued by their organizations and

what exactly does a data analyst do

put simply a data analyst is someone who

collects

transforms and organizes data in order

to help make informed decisions

besides the role itself one of the most

exciting parts of being a data analyst

is the number of opportunities available

the demand for data analysts

is greater than the number of qualified

people to fill these job openings

and this certificate program is a great

first step

in your journey to finding a job you

love

data analysts come from many different

backgrounds and have all kinds of life

experiences

you don't need decades of work

experience or an expensive education

to get started many data analysts

taught themselves the skills they needed

to land their first job

just like you're doing right now ok

now let's talk more about what you're

going to learn

the google data analytics certificate is

split into courses

based on different processes for data

analysis

those are ask prepare

process analyze share

and act plan to watch these videos

in order each one covers a new topic

and every topic builds on what you've

learned before

making it easy to track your progress

and you're in the driver's seat

even though you might see things

organized by weeks

everything can be completed at your own

pace so you

decide how much you want to do each day

by the end of the program you'll take

everything you've learned

and turn it into a project that you can

use to show off your skills

and while hiring managers at your job

interviews

now along the way you'll also hear from

googlers

that's what we call people who work here

at google

they'll give you an inside look at what

it's like to work in our industry

and share personal stories of how they

got into the field

they'll also give you some excellent

tips on how to land your dream job

stay tuned some of them are going to

introduce themselves

in just a sec so i'm angie

i'm a program manager of engineering at

google

i truly believe that cleaning data is

the heart and soul

of data it's how you get to know your

data its quirks its flaws

its mysteries i love a good mystery and

it felt like a superpower almost like i

was a detective and i'd gone in there

and i'd really solved something

hi i'm alex i'm a research scientist at

google

i research the different impacts of

artificial intelligence on society

and our users so my name is lyla jones

and

i am a part of our cloud team i get a

chance to lead a team of amazing

individuals that are focused on helping

customers get to the cloud

hi i'm evan i'm a learning portfolio

manager here at google

and i have one of the coolest jobs in

the world where i get to look at all the

different technologies that affect

big data and then work them into

training courses like this one

for students to take i'll be your

instructor for the first course

i'll take you through each module that

will cover a specific topic

in a few different ways you'll have

videos reading materials

quizzes hands-on activities and

discussion prompts

for you to chat about with other

students in an online

forum i'm really excited to be guiding

you through this course

but i'm especially excited that you've

chosen this adventure

lifelong learning is something that i'm

very passionate about

growing up when i looked around i often

didn't see many options available to me

it wasn't until i started getting

serious about my education

that i realized i had the control to

make my own opportunities

with education being the key that would

open those doors

the more i learned and the harder i

worked the more possibilities opened up

had i not gone after that knowledge and

continued challenging myself

i may not be where i am today learning

allowed me to grow

personally be successful visit places

i would never have seen and meet people

i would never have known

and now i'm going to introduce some of

those great people

hello i'm ximena financial analyst i'll

be helping you learn how to ask the

right questions about data

the project you work on and the problems

you're trying to solve

hey my name is halle analytical lead i

am so excited to show you

how to prepare your data so it's ready

for analysis

hello i'm sally measurement and

analytical lead

together we'll cover how to process and

clean your data cleaning data doesn't

require soap and water

i'm talking about making sure your data

is complete correct

and relevant to the problem you're

trying to solve

hey i'm ayanna global insights manager

we'll be digging into analysis you'll

learn how to collect

transform and organize data so that you

can use it to discover

useful information draw conclusions and

make great decisions

my name is kevin and with my experience

as director of analytics at google i'll

guide you through what i think is the

most exciting part of the data analysis

process

plan create and present effective and

compelling data visualizations

hello my name is carrie i can't wait to

tell you about all the exciting things

you can do with the programming language

r are you ready

hi i'm rishi global analytics skills

curriculum manager

i'm going to help you bring together

everything you learn during this program

by creating a case study that will

dazzle any hiring manager just like the

capstone of a great building shows

everyone that it's complete

your case study will signify your own

great achievement

of earning a google certificate in data

analytics

okay are you getting excited about the

potential of becoming a data analyst

so much as possible with data you're

about to enter

a whole new world ready let's go

[Music]

data data data i can't make bricks

without clay any guesses who said this

i'll give you a hint it wasn't a famous

tech ceo

or a data analyst the person who said

this

lived long before tech companies even

existed

but i bet you still heard of him this

line was said by sherlock holmes

the famous detective created by sir

arthur conan doyle

what doyle meant was that holmes

couldn't draw

any conclusions which would be the

bricks he mentioned

without data or the clay

you're probably not here to become a

world famous detective but data is still

the building block

that you'll use for everything you do in

your new data analyst career

sherlock holmes would agree by starting

this program

you've shown that you and sherlock

holmes have something in common

you both have an interest in learning

more that's one of the most important

qualities that data analysts can have

now there are a bunch of different ways

to explore data

but one of the great things about data

analytics is that you can often learn

how you want

when you want that might mean doing your

own research

talking with people in the industry or

taking online courses

with that said welcome to your first

course

this is your introduction to the

wonderful world of data analytics

since data analytics is the science of

data

you use this course to begin to learn

all about

data data is basically a collection of

facts or information

and through analysis you'll learn how to

use the data to draw conclusions

and make predictions and decisions

personally

i didn't jump right into the data

analytics field

i thought data analysis was for computer

engineers

instead i started off with dreams of

working in finance

once i got through an internship though

i realized

it wasn't the career path i wanted to

take i started to learn about financial

planning

and analysis and all of the work finance

analysts were doing with data

i realized that finance analysts are

really just data analysts

working in a finance department these

analysts

were helping to guide business decisions

by knowing how to use data

it was then i realized how powerful data

is

and i started to embrace it soon enough

i realized i could do this data analysis

myself

data analytics is a big open world of

opportunity

there are so many areas your analysis

skills can be applied

and in all kinds of different ways

if you're new to this world you'll learn

how to identify which

path in industry might suit your skills

and your interests the best and for

those of you

who already have some experience we'll

help you open

doors to new and exciting opportunities

one of the skills you'll gain from the

program is how to follow the best

practices that analysts use

to help make data-driven decisions

computers are one part of the process

but analysts rely on so much more to

make decisions

that's why learning how to think

analytically and using your other skills

and traits on the job

will make your work easier i know you

already know how to make good decisions

you chose to be here after all in this

first course

you'll learn more about each phase of

the data analysis process

ask prepare process analyze

share and act as a data analyst

you'll go through these steps as you use

data to inform your decisions

eventually you'll see how this program

itself

is in a way its own version of this

process

while i know you'll enjoy watching these

videos your trip to the first course

will include a whole lot more

other videos will take the form of

vignettes where you'll learn from data

analytics professionals

who are already established in their

careers they'll offer words of wisdom

as well as tales of their own

experiences starting off

on their career path you'll start your

own data journal

that will help you keep track of what

you've learned throughout the course

you'll also add your own thoughts about

what you're learning as well

throughout the program your read up on

how to navigate this program

in the world of data analytics you'll

complete activities

including some that will help you get in

the mindset of a data analyst

along the way you'll also have the

chance to connect with your fellow

learners

discussion prompts will give you a

chance to share your thoughts and at the

same time

see what your peers think about all that

you're learning

these prompts will help you build a

community support system

to use throughout the program alright

enough talking

let's get started on this exciting path

your next step

awaits welcome back

at this point you've been introduced to

the world of data analytics

and what data analysts do you've also

learned how this course will prepare you

for a successful career as an analyst

coming up

you'll learn all the ways data can be

used and you'll discover why data

analysts

are in such high demand i'm not

exaggerating when i say

every goal and success that my team and

i have achieved

couldn't have been done without data

here at google

all of our products are built on data

and data driven decision making

from concept to development to launch

we're using data to figure out the best

way forward

and we're not alone countless other

organizations also see the incredible

value in data

and of course the data analysts who help

them make use of it

so we know data opens up a lot of

opportunities

but to help you wrap your head around

all the ways you can actually use data

let's go over a few examples from

everyday life

you might not realize it but people

analyze data all the time

for instance i'm a morning person a long

time ago i realized that i'm happier and

more productive

if i get to bed early and wake up early

i came to this conclusion after noticing

a pattern

in my day-to-day experiences when i got

seven hours of sleep

and woke up at 6 30 i was the most

successful

so i thought about the relationship

between this pattern and my daily life

and i predicted that early to bed early

to rise

would be the right choice for me and i'm

definitely my best self

when i wake up bright and early i bet

you've identified patterns and

relationships in your life too

maybe about your own sleep cycle or how

you feel after eating certain foods

or what time of day you like to work out

all of these are great examples of real

life patterns and relationships

that you can use to make predictions

about the right actions to take

and that is a huge part of data analysis

right there now let's put this process

into a business setting

you may remember from an earlier video

that there's a ton of data out there

and every minute of every hour of every

day

more data is being created businesses

need a way to control

all that data so they can use it to

improve processes

identify opportunities and trends launch

new products

serve customers and make thoughtful

decisions for businesses to be on top of

the competition

they need to be on top of their data

that's why these companies

hire data analysts to control the waves

of data they collect every day

make sense of it and then draw

conclusions or make predictions

this is the process of turning data into

insights

and it's how analysts help businesses

put all their data to good use

this is actually a good way to think

about analysis turning data into

insights

as a reminder the more detailed

definition you learned earlier

is that data analysis is the collection

transformation

and organization of data in order to

draw conclusions

make predictions and drive informed

decision making

so after analysts have created insights

from data what happens

well a lot those insights are shared

with others

decisions are made and businesses take

action

and here's where it can get really

exciting data analytics

can help organizations completely

rethink something they do

or point them in a totally new direction

for example

maybe data leads them to a new product

or unique service

or maybe it helps them find a new way to

deliver an incredible customer

experience

it's these kinds of aha moments that can

help businesses reach another level

and that makes data analysts vital to

any business

now that you know more of the amazing

ways data is being used every day

you can see why data analysts are in

such high demand

we'll continue exploring how analysts

can transform data into insights that

lead to action

and before you know it you'll be ready

to help any organization

find new and exciting ways to transform

their data

hi i'm cassie and i lead decision

intelligence for google cloud

decision intelligence is a combination

of applied data science and the social

and managerial sciences

and it is all about harnessing the power

and beauty of data

i help google cloud and its customers

turn their data

into impact and make their businesses

and the world better

a data analyst is an explorer a

detective and an

artist all rolled into one

analytics is the quest for inspiration

you don't know what's going to inspire

you before you explore

before you take a look around and so

when you begin

you have no idea what you're gonna find

and

whether you're even gonna find anything

you have to

bravely dive into the unknown

and discover what lies in your data

there is

a pervasive myth that someone who works

in data should know the

everything of data and i think that

that's

unhelpful because the universe of data

has

expanded and it's expanded so much

that specialization becomes important

it's very very difficult for one person

to know and be

the everything of data and so that's why

we need these different roles

and the advice that i give folks who are

entering the space

is to pick their specialization based on

which flavor which type of impact best

suits their personality now data science

the discipline of making data useful

is an umbrella term that encompasses

three disciplines machine learning

statistics and analytics these are

separated by

how many decisions you know you want to

make before you begin with them

so if you want to make a few important

decisions under uncertainty

that is statistics if you want to

automate in other words make many many

many

decisions under uncertainty that

is machine learning and ai but what if

you don't know how many

decisions you want to make before you

begin what if what you're looking for

is inspiration you want to encounter

your unknown unknowns

you want to understand your world that

is analytics when you're considering

data science

and you're choosing which area to

specialize in i recommend

going with your personality which of the

three

excellences in data science feels like a

better fit for you

the excellence of statistics is rigor

statisticians are essentially

philosophers epistemologists

so they are very very careful about

protecting decision makers from coming

to the wrong conclusion

so if that care and rigor is

what you are passionate about i would

recommend statistics

performance is the excellence of the

machine learning and ai engineer

you know that's the one for you if

someone says to you i bet that you

couldn't build an automation system

that performs this task with 99.9999

accuracy and your response to that is

watch me

how about analytics the excellence of an

analyst

is speed how quickly

can you surf through vast amounts of

data to explore it

and discover the gems the beautiful

potential insights

that are worth knowing about and

bringing to your decision makers

are you excited by the ambiguity of

exploration

are you excited by the idea of

working on a lot of different things

looking at a lot of different data

sources and

thinking through vast amounts of

information while promising

not to snooze past the important

potential insights

are you okay being told here is a whole

lot of data

no one has looked at it before go find

something interesting

and do you thrive on creative open-ended

projects

if that's you then analytics is probably

the best fit for you

a piece of advice that i have for

analysts getting started on this journey

is it can be pretty scary to explore the

unknown

but i suggest letting go a little bit of

any temptations towards perfectionism

and instead enjoying the fun the thrill

of exploration don't worry about right

answers

see how quickly you can unwrap this gift

and find out if there is anything fun in

there

it's like your birthday unwrapping a

bunch of things some of them you like

some of them you won't but isn't it fun

to know

what's actually in there

hello again you've already learned about

being a data analyst

and how this program will help prepare

you for your future career

now it's time to explore the data

ecosystem find out where data analytics

fits into that system

and go over some common misconceptions

you might run into

in the field of data analytics to put it

simply

an ecosystem is a group of elements that

interact with one another

ecosystems can be large like the jungle

in a tropical rainforest

or the australian outback or tiny

like tadpoles in a puddle or bacteria on

your skin

and just like the kangaroos and koala

bears and the australian outback

data lives inside its own ecosystem too

data ecosystems are made up of various

elements that

interact with one another in order to

produce manage

store organize analyze and share data

these elements include hardware and

software tools

and the people who use them people like

you

data can also be found in something

called the cloud

the cloud is a place to keep data online

rather than on a computer hard drive

so instead of storing data somewhere

inside your organization's network

that data is accessed over the internet

so the cloud is just a term we use to

describe the virtual location

the cloud plays a big part in the data

ecosystem and as a data analyst

it's your job to harness the power of

that data ecosystem

find the right information and provide

the team with analysis that helps them

make smart decisions

for example you can tap into your retail

storage database

which is an ecosystem filled with

customer names

addresses previous purchase and customer

reviews

as a data analyst you could use this

information to predict

what these customers will buy in the

future and make sure the store

has the products in stock when they're

needed as another example

let's think about a data ecosystem used

by a human resources department

this ecosystem would include information

like postings from job websites

stats on the current labor market

employment rates

and social media data on prospective

employees

a data analyst could use this

information to help the team recruit new

workers

and improve employee engagement and

retention rates

but data ecosystems aren't just for

stores and offices

they work on farms too agricultural

companies

regularly use data ecosystems that

include information

including geological patterns and

weather movements

data analysts can use this data to help

farmers predict

crop yields some data analysts are even

using data ecosystems to save

real environmental ecosystems at the

scripps institution

of oceanography coral reefs all over the

world

are monitored digitally so they can see

how organisms

change over time track their growth and

measure

any increases or declines in individual

colonies

the possibilities are endless okay

now let's talk about some common

misconceptions you might come across

first is the difference between data

scientists and data analysts

it's easy to confuse the two but what

they do is actually very different

data science is defined as creating new

ways of modeling and understanding the

unknown

by using raw data here's a good way to

think about it

data scientists create new questions

using data while

analysts find answers to existing

questions by creating

insights from data sources there are

also

many words and phrases you'll hear

throughout this course that are easy to

get mixed up

for example data analysis and data

analytics

sound the same but they're actually very

different things

let's start with analysis you've already

learned that data analysis

is the collection transformation and

organization

of data in order to draw conclusions

make predictions

and drive informed decision making data

analytics

in the simplest terms is the science of

data

it's a very broad concept that

encompasses everything from the job of

managing and using data

to the tools and methods that data

workers use each and every day

so when you think about data data

analysis

and the data ecosystem it's important to

understand

that all of these things fit under the

data analytics

umbrella all right now that you know a

little more

about the data ecosystem and the

differences between data analysis and

data analytics

you're ready to explore how data is used

to make effective decisions

you'll get to see data driven decision

making in action

so far you've discovered that there are

many different ways

data can be used in our everyday lives

we use

data when we wear a fitness tracker or

read product reviews to make a purchase

decision

and in business we use data to learn

more about our customers

improve processes and help employees do

their jobs more effectively

but this is just the tip of the iceberg

one of the most powerful ways you can

put data to work

is with data-driven decision-making

data-driven decision-making is defined

as using facts

to guide business strategy organizations

in many different

industries are empowered to make better

data-driven decisions

by data analysts all the time the first

step in data-driven decision making

is figuring out the business need

usually

this is a problem that needs to be

solved for example

a problem could be a new company needing

to establish better brand recognition

so it can compete with bigger more

well-known competitors

or maybe an organization wants to

improve a product and needs to figure

out how to source parts from a more

sustainable or ethically responsible

supplier

or it could be a business trying to

solve the problem

of unhappy employees low levels of

engagement

satisfaction and retention whatever the

problem is

once it's defined a data analyst finds

data

analyzes it and uses it to uncover

trends

patterns and relationships sometimes the

data-driven strategy

will build on what's worked in the past

other times

it can guide a business to branch out in

a whole new direction

let's look at a real world example think

about a music or movie streaming service

how do these companies know what people

want to watch or listen to

and how do they provide it well using

data driven decision making

they gather information about what their

customers are currently listening to

analyze it then use the insights they've

gained

to make suggestions for things people

will most likely

enjoy in the future this keeps customers

happy and coming back for more which in

turn means

more revenue for the company another

example of data-driven decision-making

can be seen in the rise of e-commerce it

wasn't long ago

that most purchases were made in a

physical store

but the data showed people's preferences

were changing

so a lot of companies created entirely

new business models

that remove the physical store and let

people shop right from their computers

or mobile phones with products delivered

right to their doorstep

in fact data-driven decision making can

be so powerful

it can make entire business methods

obsolete

for example data help companies

completely move away from corded phones

and replace them with mobile phones by

ensuring that data

is built into every business strategy

data analysts

play a critical role in their company's

success

but it's important to note that no

matter how valuable data driven decision

making

is data alone will never be as powerful

as data combined with human experience

observation

and sometimes even intuition to get the

most out of data driven decision making

it's important to include insights from

people who are familiar with the

business problem

these people are called subject matter

experts and they have the ability to

look at the results of data analysis

and identify any inconsistencies make

sense of gray areas

and eventually validate choices being

made

organizations that work this way put

data at the heart of every business

strategy

but also benefit from the insights of

their people

it's a win-win as a data analyst you

play a key role in empowering these

organizations to make data-driven

decisions

which is why it's so important for you

to understand how data plays a part

in the decision-making process

we've covered a lot and i'm sure you

have so much to think about

already that's a good thing it means you

started collecting data

and you're doing your own personal

analysis that's what it's all about

you've built a great base already as

this course continues

your knowledge and data analysis skills

will continue to grow

once you've established a solid

foundation you'll apply what you've

learned

to the rest of the program the data

analysis process

will help provide a framework for

everything you do

soon you'll take your first graded

assessment

it's a great way to check your

understanding of the concepts

and build confidence in your knowledge

everyone learns at different speeds

so take your time get familiar with the

concepts

as soon as you feel ready you can go

ahead and get started

keep in mind if at any point you're not

sure about a question

you can always review the videos and

readings to remind yourself of the

answer

we're all about open book tests here

once you've passed

you'll be all set to move on you've got

this

before you know it you'll be done with

all of the courses

and you'll be ready to create your own

case study

then if it's what you want to do you'll

start your job search

equipped with the tools and skills that

will wow any company

you talk to i can't wait to see where

you go

with data analytics for now though give

yourself a pat on the back

for a job well done see you soon

welcome now that you have a solid

foundation on the basics of data

it's time to focus on some particular

skills and characteristics

that would be key to your future career

as a data analyst

we'll begin with five key skills move on

to the characteristics of analytical

thinking

and then learn how data analysts balance

their roles and responsibilities

along the way you'll also discover how

to tap into your own natural abilities

for strategy

technical expertise and data design

these are incredibly helpful skills to

have and you'll learn how to make them

even stronger

finally you'll be introduced to some

fascinating real world examples

of how data is influencing the lives of

people all around the world

alright let's get started

earlier i told you that you already have

analytical skills you just might not

know it yet

when learning new things sometimes

people overlook their own skills

but it's important you take the time to

acknowledge them

especially since these skills are going

to help you as a data analyst

in fact you're probably more prepared

than you think

don't believe me well let me prove it

let's start by defining what i'm talking

about here

analytical skills are qualities and

characteristics

associated with solving problems using

facts

there are a lot of aspects to analytical

skills but

we'll focus on five essential points

their curiosity

understanding context having technical

mindset

data design and data strategy now

you may be thinking i don't have these

kinds of skills

or i only have a couple of them

but stay with me and i bet you'll change

your mind

let's start with curiosity curiosity is

all about

wanting to learn something curious

people usually seek out new challenges

and experiences this leads to knowledge

the very fact that you're here with me

right now

demonstrates that you have curiosity

all right that was an easy one

now think about understanding context

context is the condition in which

something exists or happens

this can be a structure or an

environment a simple way of

understanding context

is by counting to five one

two three four

five all of those numbers exist in the

context

of one through five but what if a friend

of yours

said to you one two four five three

well the three would be out of context

simple right

but it can be a little tricky there's a

good chance that you might not even

notice

the three being out of context if you

aren't paying close attention

that's why listening and trying to

understand the full picture is critical

in your own life you put things into

context all the time

for example let's think about your

grocery list

if you group together items like flour

sugar and yeast

that's you adding context to your

groceries

this saves you time when you're at the

baking out at the grocery store

let's look at another example have you

ever shuffled a deck of cards and

noticed a joker

if you're playing a game that doesn't

include jokers identifying that card

means

you understand it's out of context

remove it

and you're much more likely to play a

successful game alright

so now we know you have both curiosity

and the ability to understand context

let's move on to the third skill a

technical mindset

a technical mindset involves the ability

to break things down

into smaller steps or pieces and work

with them

in an orderly and logical way for

instance

when paying your bills you probably

already break down the process into

smaller steps

maybe you start by sorting them by the

date they're due

next you might add them up and compare

that amount

to the balance in your bank account this

would help you see

if you can pay your bills now or if you

should wait until the next paycheck

finally you'd pay them when you take

something that seems like a single task

like paying your bills and break it into

smaller steps

with an orderly process that's using a

technical mindset

now let's explore the fourth part of an

analytical skill set

data design data design is how you

organize information

as a data analyst design typically has

to do with an

actual database but again the same

skills can easily be applied to everyday

life

for example think about the way you

organize the contacts in your phone

that's actually a type of data design

maybe

you list them by first name instead of

last or maybe you use email addresses

instead of their names

what you're really doing is designing a

clear logical list that lets you call or

text the contact

in a quick and simple way the last

but definitely not least the fifth and

final element of analytical skills is

data strategy

data strategy is the management of the

people processes

and tools used in data analysis let's

break that down

you manage people by making sure they

know how to use the right data

to find solutions to the problem you're

working on for processes

it's about making sure the path to that

solution is clear

and accessible and for tools you make

sure the right technology is being used

for the job

now you may be doubting my ability to

give you an example from real life that

demonstrates data strategy

but check this out imagine mowing a lawn

step one would be

reading the owner's manual for the mower

that's making sure the people involved

well you in this example know how to use

the data available

the manual would instruct you to put on

protective eyewear and closed-toed shoes

then it's on to step two making the

process

the path clear and accessible

this would involve you walking around

the lawn picking up large sticks or

rocks that might get in your way

finally for step three you check the

lawnmower

your tool to make sure it has enough gas

and oil

and is in working condition so the lawn

can be moaned safely

so there you have it now you know the

five essential skills

of a data analyst curiosity

understanding context having a technical

mindset

data design and data strategy

i told you that you are already an

analytical thinker

now you can start actively practicing

these skills

as you move through the rest of this

course curious about what's next

move on to the next video

now that you know the five essential

skills of a data analyst

you're ready to learn more about what it

means to think analytically

people don't often think about thinking

thinking

is second nature to us it just happens

automatically

but there are actually many different

ways to think

some people think creatively some think

critically

and some people think in abstract ways

let's talk about analytical thinking

analytical thinking involves identifying

and defining a problem

and then solving it by using data in an

organized

step-by-step manner so as data analysts

how do we think analytically well to

answer that question

we will now talk about a second set of

five

the five key aspects to analytical

thinking

they are visualization strategy problem

orientation

correlation and finally big picture and

detail-oriented thinking

let's start with visualization in data

analytics

visualization is the graphical

representation of information

some examples include graphs maps or

other design elements

visualization is important because

visuals

can help data analysts understand and

explain information more effectively

think about it like this if you are

trying to explain the grand canyon to

someone

using words would be much more

challenging than showing them a picture

a visualization of the grand canyon

would help you make your point much

quicker

now let's talk about the second part of

analytical thinking

being strategic with so much data

available

having a strategic mindset is key to

staying focused

and on track strategizing helps data

analysts see

what they want to achieve with the data

and how they can get there

strategy also helps improve the quality

and usefulness

of the data we collect by strategizing

we know all our data is valuable and can

help us accomplish our goals

next up on the analytical thinking

checklist being problem oriented

data analysts use a problem-oriented

approach

in order to identify describe and solve

problems

it's all about keeping the problem top

of mind throughout the entire project

for example say a data analyst is told

about the problem

of a warehouse constantly running out of

supplies

they will move forward with different

strategies and processes

but the number one goal would always be

solving the problem

of keeping inventory on the shelves data

analysts

also ask a lot of questions this helps

improve communication and saves time

while working on a solution an example

of that would be

surveying customers about their

experiences using a product

and building insights from those

questions to improve that product

this leads us to the fourth quality of

analytical thinking

being able to identify a correlation

between two or more pieces of data

a correlation is like a relationship

you can find all kinds of correlations

in data maybe it's the relationship

between

the length of your hair and the amount

of shampoo you need

or maybe you notice a correlation

between a rainier season

leading to a high number of umbrellas

being sold

but as you start identifying

correlations and data

there's one thing you always want to

keep in mind correlation does not

equal causation in other words

just because two pieces of data are both

trending in the same direction

that doesn't necessarily mean they are

all related

we'll learn more about that later and

now the final piece

of the analytical thinking puzzle big

picture thinking

this means being able to see the big

picture as well as the details

a jigsaw puzzle is a great way to think

about this big picture thinking

is like looking at a complete puzzle you

can enjoy the whole picture

without getting stuck on every tiny

piece that went into making it

if you only focus on individual pieces

you wouldn't be able to see

past that which is why big picture

thinking

is so important it helps you zoom out

and see possibilities and opportunities

this leads to exciting

new ideas or innovations on the flip

side

detail-oriented thinking is all about

figuring out

all of the aspects that will help you

execute a plan

in other words the pieces that make up

your puzzle

there are all kinds of problems in the

business world that can benefit

from employees who have both a big

picture and a detail-oriented way of

thinking

most of us are naturally better at one

or the other but you can always develop

the skills to fit

both pieces together and now that you

know the five aspects of analytical

thinking

visualization strategy problem

orientation

correlation and big picture and

detail-oriented thinking

you can put them to work for you when

you're working with data

and as you continue through this course

you'll learn how

let's recap what we've learned about

analytical thinking so far

the five key aspects are visualization

strategy

problem orientation correlation and

using big picture

and detail-oriented thinking and we've

seen how you already use them

in your everyday life we also talked

about how different people

naturally use certain types of thinking

but that you can absolutely grow

and develop the skills that might not

come as easily to you

this means you can become a versatile

thinker which is a very important

part of data analysis you might

naturally be an analytical thinker

but you can learn to think creatively

and critically

and be great at all three the more ways

you can think

the easier it is to think outside the

box and come up with fresh ideas

but why is it important to think in

different ways well because in data

analysis

solutions are almost never right in

front of you you need to think

critically to find out

the right questions to ask but you also

need to think creatively

to get new and unexpected answers let's

talk about some of the questions

data analysts ask when they're on the

hunt for a solution

here's one that will come up a lot what

is the root cause of a problem

a root cause is the reason why a problem

occurs

if we can identify and get rid of a root

cross

we can prevent that problem from

happening again

a simple way to wrap your head around

root causes is with the process called

the five wise in the five whys you ask

why five times to reveal the root cause

the fifth and final answer should give

you some useful

and sometimes surprising insights here's

an example of the five why's in action

let's say you wanted to make a blueberry

pie but couldn't find any blueberries

you'd be trying to solve a problem by

asking why can't i make a blueberry pie

the answer would be there are no

blueberries at the store

there's why number one so you then ask

why were there no blueberries at the

store and discover

that the blueberry bushes don't have

enough fruit this season

that's why number two next you'd ask

why was there not enough fruit this

would lead to the fact that birds were

eating all the berries

why number three asked and answered

now we get to why number four ask

why a fourth time and the answer would

be that although the birds

normally prefer mulberries and don't eat

blueberries

the mulberry bushes didn't produce fruit

this season

so the birds are eating blueberries

instead and finally

we get to why number five which should

reveal the root cause

a late frost damaged the mulberry bushes

so they didn't produce any fruit

so you can't make a blueberry pie

because of a late frost

months ago see how the five wives can

reveal

some very surprising root causes this is

a great trick to know

and it can be a very helpful process in

data analysis

another question commonly asked by data

analysts

is where are the gaps in our process

for this many people will use something

called gap analysis

gap analysis lets you examine and

evaluate how a process works currently

in order to get where you want to be in

the future

businesses conduct gap analysis to do

all kinds of things

such as improve a product or become more

efficient the general approach to gap

analysis

is understanding where you are now

compared to where you want to be

then you can identify the gaps that

exist between a current and future state

and determine how to bridge them a third

question that data analysts ask a lot is

what did we not consider before this is

a great way to think about

what information or procedure might be

missing from a process

so you can identify ways to make better

decisions and strategies

moving forward these are just a few

examples of the kinds of questions data

analysts use

at their jobs every day as you begin

your career

i'm sure you'll think of a whole lot

more the way data analysts think and ask

questions plays a big part in how

businesses make decisions

that's why analytical thinking and

understanding how to ask the right

questions

can have such a huge impact on the

overall success of a business

later we'll talk more about how

data-driven decisions

can lead to successful outcomes

in an earlier video you learned about

five essential analytical skills

as a reminder their curiosity

understanding context

having a technical mindset data design

and data strategy in the next couple of

videos

we'll explore how these abilities all

become part of data-driven decision

making

but first let's look at the concept of

data-driven decision making

and why it's more likely to lead to

successful outcomes

you might remember that data driven

decision making involves using facts

to guide business strategy data analysts

can tap into the power of data

to do all kinds of amazing things with

data

they can gain valuable insights verify

their theories or assumptions

better understand opportunities and

challenges support an objective

help make a plan and much more

in business data-driven decision making

can improve the results in a lot of

different ways

for example say a dairy farmer wants to

start making

and selling ice cream they could guess

what flavors customers would like

but there's a better way to get the

information the farmer could survey

people

and ask them what flavors they prefer

this gives the farmer the data they need

to pick ice cream flavors people will

enjoy

here's another example let's say the

president of an organization

is curious about what perks employees

value most

she asked the human resources director

who says people value

casual dress code it's a gut feeling but

the hr director backs it up with the

fact that

he sees a lot of people wearing jeans

and t-shirts

but what if this company were to use a

more structured employee feedback

process

such as a survey it might reveal that

employees

actually enjoy free public

transportation cards the most

the human resources director just didn't

realize that because

he drives to work these are just some of

the benefits

of data-driven decision making it gives

you greater confidence about your choice

and your abilities to address business

challenges it helps you become more

proactive

when an opportunity presents itself and

it saves you time and effort when

working towards a goal

now let's learn more about how these

five skills help you tap into all the

potential of data-driven decision-making

first think about curiosity and context

the more you learn about the power of

data the more curious you're likely to

become

you'll start to see patterns in

relationships in everyday life

whether you're reading the news watching

a movie or going to an appointment

across town

the analysts take their thinking in a

step further

by using context to make predictions

research

answers and eventually draw conclusions

about what they've discovered

this natural process is a great first

step in becoming more data driven

having a technical mindset comes next

everyone has

instincts or as in the case of our human

resources director example

gut feelings data analysts are no

different

they have gut feelings too but they've

trained themselves

to build on those feelings and use a

more technical approach to explore them

they do this by always seeking out the

facts putting them to work

through analysis and using the insights

they gain to make informed decisions

next we come to data design which has a

strong connection to data driven

decision making

to put it simply designing your data so

that is organized in a logical way

makes it easy for data analysts to

access understand

and make the most of available

information

and it's important to keep in mind that

data design

doesn't just apply to databases this

kind of thinking

can work with all sorts of real life

situations too

the basic idea is this if you make

decisions that are informed by data

you are more likely to make more

informed and effective decisions

the final ability is data strategy which

incorporates the people

processes and tools used to solve a

problem this is a big one to remember

because

data strategy gives you a high level

view of the path

you'll need to take to achieve your

goals also

data-driven decision making isn't a

one-person job

it's much more likely to be successful

if everyone is on board

and on the same page so it's important

to make sure

specific procedures are in place and

that your technology being used

is aligned with your data-driven

strategy

now you know how these five essential

analytical skills work towards making

better

data-driven decisions so far many of the

examples you've heard are hypothetical

that means they could be true in theory

but aren't specific

real world cases next we'll look at some

real examples

i can't wait to share how data analysts

put data to work

for amazing results

in this video i'm going to share some

case studies

that highlight the incredible work data

analysts do

each of these scenarios shows off the

power of data driven decision making

in unexpected ways the first story is

about google

as i mentioned a little while back here

at google

our mission is to organize the world's

information and make it universally

accessible

and useful all of our products from idea

to development

to launch are built on data and data

driven decision making

there are tons of examples here at

google of people using facts to create

business strategy

but one of the most famous ones has to

do with google's human resources

so here's how it went the hr department

wanted to know

if there was value in having managers

were their contributions worthwhile

or should everyone just be an individual

contributor

to answer that question google's people

analytics team

look at past performance reviews and

employee surveys

the data they found was plotted on a

graph because

as you've learned visuals are extremely

helpful when trying to understand a

problem or concept

the graph revealed that googlers had

positive feelings about their managers

but the data was pretty general and the

team wanted to learn more

so they dug deeper and split the data

into quartiles

a quartile divides data points into four

equal parts

or quarters here's where the really cool

stuff started happening

the data analyst discovered that there

was a big difference between the very

top

and the very bottom quartiles as it

turned out

the teams with the best managers were

significantly happier

more productive and more likely to want

to keep working at google

this confirmed that managers were valued

and make a big difference therefore the

idea of having only individual

contributors

was not implemented but there was still

more work to do

just knowing that great managers create

great results doesn't lead to actionable

insights

you have to identify what exactly makes

a great manager

so the team took two additional steps to

collect more data

first they launched an awards program

where employees could nominate their

favorite managers

for every submission you had to provide

examples or data

about what makes that manager great the

second step involved

interviewing managers who are graft on

the top

and bottom quartiles this helped the

analytics team

see the differences between successful

and less successful management behaviors

the best behaviors were identified as

were the most common reasons for a

manager needing improvement

the final step was sharing these

insights and putting a procedure in

place

for evaluating managers with these

qualities in mind

this data-driven decision continues to

create an exceptional company culture

for my colleagues and me thanks data

another interesting example comes from

the nonprofit sector

nonprofits are organizations dedicated

to advancing

a social cause or advocating for a

particular effort

such as food security education or the

arts

in this case data analysts research how

journalists can make a more meaningful

impact for the non-profits they would

write about because journalists write

for newspapers

magazines and other news outlets they

can help non-profits reach readers like

you and me

who then take action to help non-profits

reach their goals

for instance say you read about the

problem of climate change

in an online magazine if the article is

effective

you learn more about the cause and might

even be compelled to make greener

choices in your day-to-day life

volunteer for a non-profit or make a

donation

that's an example of the journalists

work bringing about awareness

understanding and engagement so

back to the story the data analysts used

the tracker to monitor story topics

clicks web traffic comments shares

and more then they evaluated the

information

to make recommendations for how the

journalists could do their jobs even

better

in the end they came up with some great

ideas for how nonprofits

and journalists can motivate people

everywhere to work together

and make the world a better place

there's really no end to what you can do

as a data analyst

as you progress through this program

you'll discover even more possibilities

great job following along with the

topics in these past few videos

you learned all about analytical skills

and the five key characteristics of data

analysts

you probably even learned that you're a

pro at most of these already

next you discovered what it means to

think analytically

and the specific skills data analysts

develop

to help them do it you explore tools and

processes

that enable data analysts to pinpoint a

problem and ask the right questions in

order to solve it

finally some real world stories helped

illustrate why data-driven

decision-making

is usually more successful than other

methods you're building a wonderful

foundation for your career as a data

analyst

with every video your skills will

continue to expand

and your understanding of key data

analytics concepts will only get

stronger

soon you'll have a chance to test out

everything you've learned

this is a really useful opportunity to

check your understanding of

all the concepts we've discussed and if

you're ever unsure about a question

you can review the videos and readings

to find the answer

this is another awesome way to practice

collecting data

keep up the great work

hey it's great to have you back so we've

talked a little bit about the data

analysis process as a quick refresher

the data analysis process phases are ask

prepare process analyze

share and act you might remember me

saying earlier

that this entire program is modeled

after these steps

so now we're going to really dig in and

explore how each of these phases work

together

but i'm getting a little ahead of myself

first let's spend a little time

understanding the data life cycle no

data isn't actually alive but it does

have a life cycle

so how do data analysts bring data to

life

well it starts with the right data

analysis tool

these include spreadsheets databases

query languages

and visualization software don't worry

if you don't know how these work or even

what they are

at one point every data analyst has been

right where you are

right now and they probably had a lot of

the same questions

i remember when i first started learning

about spreadsheets

i was a young intern and the company i

was working for

was in the middle of a big systems

change that meant

we had to move tons of reports from the

old system

to the new one after a few weeks i

noticed that

even the people who are further in their

careers

were not as technically minded as i was

so that became a great opportunity for

me to add value

my aha spreadsheet moment came when i

started researching shortcuts

that i could use to work with the

spreadsheets more efficiently

this would really streamline the process

of getting those reports moved over to

the new system

once everything started flowing i

remember getting emails from other

finance analysts at the company

they were so grateful that someone had

come in and fixed a problem that no one

else could

that inspired me to go even further and

learn how to use spreadsheets

in all sorts of incredible ways as you

continue through this course

i bet you'll be just as impressed as i

was and before you know it

you'll bring data to life too let's get

started

here's a question for you when you think

about a life cycle

what's the first thing that comes to

mind now

i'm not a mind reader but i know

whatever you're thinking

is right there's actually no wrong

answer because

everything has a life cycle one of the

most well-known examples of a life cycle

is a butterfly butterflies begin as eggs

hatch into caterpillars and then become

a chrysalis

that's where the real magic happens data

has a life cycle of its own too

in this video we're going to talk about

each of the stages

in that life cycle to help you

understand the individual phases

data goes through the life cycle of data

is plan capture manage analyze

archive and destroy let's start with the

first phase

planning this actually happens well

before starting an analysis project

during planning a business decides what

kind of data it needs

how it will be managed throughout its

life cycle who will be responsible for

it

and the optimal outcomes for example

let's say an electricity provider wanted

to gain

insights into how to save people energy

in the planning phase

they might decide to capture information

on how much electricity its customers

use each year

what types of buildings are being

powered and what types of devices are

being powered inside of them

the electricity company would also

decide which team members will be

responsible for collecting

storing and sharing that data all of

this happens during planning

and it helps set up the rest of the

project the next phase

is when you capture data this is where

data is collected

from a variety of different sources and

brought into the organization

with so much data being created every

day the ways to collect it

are truly endless one common method is

getting data from outside resources

for example if you're doing data

analysis on weather patterns

you'd probably get data from a publicly

available data set

like the national climatic data center

another way to get data is from a

company's own documents and files

which are usually stored inside a

database while we've mentioned databases

before

we haven't gone into too much detail

about what they are

a database is a collection of data

stored in a computer system

in the case of our electricity provider

the business

would probably measure data usage among

its customers

within a database that it owns as a

quick note

when you maintain a database of customer

information ensuring data integrity

credibility and privacy are all

important concerns

you'll learn a lot more about that later

on now that we've captured our data

we move on to the next phase of the data

life cycle

manage here we're talking about how we

care for our data

how and where it's stored the tools used

to keep it safe and secure

and the actions taken to make sure that

it's maintained properly

this phase is very important to data

cleansing

which we'll cover later on next

it's time to analyze your data this is

where data analysts

really shine in this phase the data is

used to solve problems

make great decisions and support

business goals

for example one of our electricity

companies goals

might be to find ways to help customers

save energy

moving on the data lifecycle now evolves

to the archive phase archiving means

storing data in a place where it's still

available but may not be used again

during analysis analysts handle huge

amounts of data

can you imagine if we had to sort

through all of the available data that's

out there

even if it was no longer useful and

relevant to our work

it makes way more sense to archive it

than to keep it around

and finally the last step of the data

life cycle

the destroy phase yes it sounds sad

but when you destroy data it won't hurt

a bit

so let's get back to our electricity

provider example

they would have data stored on multiple

hard drives

to destroy it the company would use a

secure data erasure software

if there were any paper files they would

be shredded too

this is important for protecting a

company's private information

as well as private data about its

customers

and there you have it the data life

cycle

and now that you understand the

different phases data goes through

during its life cycle

you can better understand how to

approach the data analysis process

which we'll talk about soon

now that you understand all the phases

of the data life cycle

it's time to move on to the phases of

data analysis

they sound similar but are two different

things

data analysis isn't a life cycle it's

the process

of analyzing data coming up we'll look

at each

step of the data analysis process and

how it will relate

to your work as a data analyst even this

program is designed to follow these

steps

understanding these connections will

help guide your own

analysis and your work in this program

you've already learned that this program

is modeled after the stages

of the data analysis process this

program is split into courses

six of which are based upon the steps of

data analysis

ask prepare process

analyze share and act

okay let's start with the first step in

data analysis

the ass phase in this phase we do two

things

we define the problem to be solved and

we make sure that we fully understand

stakeholder expectations

stakeholders hold a stake in the project

there are people

who have invested time and resources

into our project

and are interested in the outcome let's

break that down

first defining a problem means you look

at the current state and identify how

it's different

from the ideal state usually there's an

obstacle we need to get rid of

or something wrong that needs to be

fixed for instance

a sports arena might want to reduce the

time fans spend

waiting in the ticket line the obstacle

is figuring out

how to get the customers to their seats

more quickly

another important part of the ass phase

is understanding

stakeholder expectations the first step

here is to determine

who the stakeholders are that may

include your manager

an executive sponsor or your sales

partners there can be lots of

stakeholders

but what they all have in common is that

they help make decisions

influence actions and strategies and

have specific goals

they want to meet they also care about

the project

and that's why it's so important to

understand their expectations

for instance if your manager assigns you

a data analysis project related to

business risk

it would be smart to confirm whether

they want to include all types of risks

that could affect the company

or just risks related to weather such as

hurricanes and tornadoes

communicating with your stakeholders is

key in making sure you stay engaged and

on track throughout the project

so as a data analyst developing strong

communication strategies is very

important

this part of the ask phase helps you

keep focused

on the problem itself not just its

symptoms

as you learned earlier the five why's

are extremely helpful here

in an upcoming course you'll learn how

to ask effective questions

and define the problem by working with

stakeholders

you'll also cover strategies that can

help you share

what you discover in a way that keeps

people interested

after that we'll move on to the prepare

step of the data analysis process

this is where data analysts collect and

store data

they'll use for the upcoming analysis

process you'll learn more about the

different types of data

and how to identify which kinds of data

are most useful

for solving a particular problem you'll

also discover why it's so important

that your data and results are objective

and unbiased

in other words any decisions made from

your analysis

should always be based on facts and be

fair and

impartial next is the process step here

data analysts find and eliminate any

errors and inaccuracies

that can get in the way of results this

usually means cleaning data

transforming it into more useful format

combining two or more data sets to make

information more complete and removing

outliers

which are any data points that could

skew the information

after that you'll learn how to check the

data you prepared to make sure it's

complete and correct

this phase is all about getting the

details right so you'll also fix

typos inconsistencies or missing in

inaccurate data

and to top it off you'll gain strategies

for verifying and sharing your data

cleansing with stakeholders

then it's time to analyze analyzing the

data you've collected involves using

tools to transform and

organize that information so that you

can draw useful conclusions

make predictions and drive informed

decision making

there are lots of powerful tools data

analysts use in their work

and in this course you'll learn about

two of them spreadsheets

and structure query language or sql

which is often pronounced sql the next

course is based on the share phase

here you'll learn how data analysts

interpret results

and share them with others to help

stakeholders make effective

data-driven decisions in the shared

phase

visualization is a data analyst's best

friend

so this course will highlight why

visualization is essential

to getting others to understand what

your data is telling you

with the right visuals facts and figures

become so much easier to see

and complex concepts become easier to

understand

we'll explore different kinds of visuals

and some great data visualization tools

you'll also practice your own

presentation skills by creating

compelling slide shows

and learning how to be fully prepared to

answer questions

then we'll take a break from the data

analysis process to show you all

of the really cool things you can do

with the programming language

r you don't need to be familiar with r

or programming languages in general just

know that r

is a popular tool for data manipulation

calculation

and visualization and for our final data

analysis phase

we have act this is the exciting moment

when the business takes

all of the insights you the data

analysts have provided

and puts them to work in order to solve

the original business problem

and will be acting on what you've

learned throughout this program

this is when you'll prepare for your job

search and have the chance to complete

a case study project it's a great

opportunity

for you to bring together everything

you've worked on throughout this course

plus adding a case study to your

portfolio helps you stand out from the

other candidates

when you interview for your first data

analyst job

now you know the different steps of the

data analysis process

and how our course reflects it you have

everything you need to understand

how this course works and my fellow

googlers and i

will be here to guide you every step of

the way

regardless of what type of data analysis

you're conducting

the process is generally the same the

example that i'll walk through

is that of our employee engagement

survey but you can imagine that this

process applies to just about any data

analysis

that you're going to conduct as an

analyst the first thing you want to do

is

ask you want to ask all of the right

questions at the beginning of the

engagement

so you better understand what your

leaders and stakeholders need from this

analysis

so the types of questions that i

generally ask are around

you know what is the problem that we're

trying to solve

what is the purpose of this analysis

what are we hoping to learn

from it so after you've asked all the

right questions and you've

wrapped your arms around the scope of

the analysis you need to conduct

the next step is to prepare we need to

be thinking about

what type of data we need to answer

those key questions this could be

anything from

quantitative data or qualitative data it

could be

cross-sectional or point in time versus

longitudinal over

a long period of time we need to be

thinking about the type of data we need

in order to answer the questions that

we've set out to answer

based on what we learned when we asked

the right questions

we also need to be thinking about how

we're going to collect that data

or if we need to collect that data it

may be the case

that we need to collect this data brand

new and so we need to think about what

type of data we're going to be

collecting and how

for our employee engagement survey we do

that via a survey of both quantitative

and qualitative questions but it may

actually be the case that for many

analyses

the data that you're looking for already

exists

then it's a question of working with

those data owners to make sure that

you're able to leverage that data and

use it responsibly

after you've done all the hard work to

collect your data

now you need to process that data it

begins with cleaning

this to me is the most fun part of the

data analytics process

you know we can think of it as the

initial introduction or the handshake

you know hello to your data this is

where you get a chance to understand

its structure its quirks its nuances

and you really get a chance to

understand deeply

what type of data you're going to be

working with and understanding what

potential that data has to answer

all of your questions this is such an

important part

too where we're running through all of

our quality assurance checks

for example do we have all of the data

that we anticipated we would have

are we missing data at random or is it

missing in a systematic way

such that maybe something went wrong

with our data collection effort

if needed did we code all of our data

the right way

are there any outliers that we need to

treat differently

you know this is the part where you

spend a lot of time

really digging deeply into the structure

and nuance of the data

to make sure that you're able to analyze

it appropriately and responsibly

after cleaning our data and running all

of our quality assurance

checks now is the point where we analyze

our data

making sure to do so in as objective and

unbiased a way as possible

to do this the first thing we do is run

through a series of analyses that we've

already planned ahead of time

based on the questions that we know we

want to answer from the very very

beginning of the process

one thing that's probably the hardest

about this particular process the

hardest thing about analyzing data

is that we as analysts are are trained

to look for

patterns and over time as we become

better and better at our jobs

what we'll often find is that we can

start to intuit what we might see in the

data

we might have a sneaking suspicion as to

what the data are going to tell

us and this is the point where we have

to take a step back

and let the data speak for itself you

know as data analysts we are

storytellers

but we also have to keep in mind that it

is not our story to tell

that story belongs to the data and it is

our job as analysts

to amplify and tell that story in as

unbiased and objective a way as possible

the next step is to share all of the

data and insights that you've generated

from your analyses now typically for our

employee engagement survey

we start by sharing the high level

findings with our executive team

we want them to have a landscape view of

how the organization is feeling

and we want to make sure that there

aren't any surprises as they dig deeper

and deeper into the data

to understand how teams are feeling and

how individual employees are feeling

all of this work from asking the right

questions to collecting your data

to analyzing and sharing doesn't mean

much of

anything if we aren't taking action on

what we've just learned

this to me is the most critical part

especially of our employee engagement

survey

i like to say that the survey is

actually the easy part

and acting on the results is really

where the real work begins

this is where we use all of those

data-driven insights to decide

what types of interventions we want to

introduce not only the organizational

level but also at the team level as well

so we might find for example that the

organization is working on a series of

of interventions to help

improve part of the employee experience

whereas individual teams have additional

roles

responsibilities to play to either

bolster some of those efforts or to

introduce new ones to sort of better

meet their team where where their

strengths and opportunity areas are

the data analysis process is rigorous

but it is lengthy and i can completely

appreciate

that we as data analysts get so excited

about just diving right into the data

and doing what we do best the challenge

is that if we don't

work through the process and in its

entirety if we try to skip

steps we're not going to be able to

elicit the

the insights that we're looking for i

absolutely love my job

i i such a deep appreciation for

for data and what it can do and and what

type of insight we can derive

from it

i'm looking forward to introducing you

to some of the tools data analysts use

each and every day

there are tons of options out there but

the most common ones you'll see analysts

use

are spreadsheets query languages and

visualization tools

and this video is going to give you a

quick look at how these tools are being

used by data analysts

every day believe it or not i was

several years into my accounting and

finance career before i saw

all of these tools working together at

that point

i was very experienced with spreadsheets

and had worked in large data sets

with some of the traditional database

programs

i had the foundational skill set to use

query languages

and i had dabbled in visualizations but

i had never brought them all together

then i got hired here at google and it

was so eye-opening to come into a place

like this

with an abundance of information

everywhere you look

as an analyst at google the true power

of these tools

became so much clearer to me i became

more focused on really maximizing

everything these tools could do

streamlining my reporting and just

making my work simpler

all of a sudden i had a lot more time

and space to dedicate to identifying new

problems to solve

and driving decision making without a

doubt

once you've learned the power of these

tools you will be well on your way to

becoming the best data analyst you can

possibly be

alright i hope that story has you even

more motivated

for this course let's get started with

spreadsheets

again there are lots of different

spreadsheet solutions

but two popular options are microsoft

excel

and google sheets to put it simply a

spreadsheet

is a digital worksheet it stores

organizes

and sorts data this is important because

the usefulness of your data

depends on how well it's structured when

you put your data into a spreadsheet

you can see patterns group informations

and easily find the information you need

spreadsheets also have some really

useful features called formulas and

functions

a formula is a set of instructions that

performs a specific calculation

using the data in a spreadsheet formulas

can do basic things like add

subtract multiply and divide but they

don't stop there

you can also use formulas to find the

average of a number set

look up a particular value return the

sum of a set of values that meets a

particular rule

and so much more a function is a preset

command

that automatically performs a specific

process

or task using the data in the

spreadsheet

that sounds pretty technical i know so

let's break it down

just think of a function as a simpler

more efficient way of doing

something that would normally take a lot

of time in other words

functions can help make you more

efficient those are the spreadsheet

basics for now

later on you'll see them in action and

start working with spreadsheets yourself

the next data analysis tool is called

query language

a query language is a computer

programming language that allows you to

retrieve and manipulate data from a

database

you'll learn something called structured

query language more commonly known

as sql sql is a language that lets data

analysts communicate

with a database a database is a

collection of data stored in a computer

system sql is the most widely used

structured query language

for a couple of reasons it's easy to

understand and works

very well with all kinds of databases

with sql data analysts can access the

data they need

by making the query although query means

question

i like to think of it as more of a

request so you're requesting that the

database do something for you

you can ask it to do a lot of different

things such as

insert delete select or update data

okay that's a top level look at sql

in a later video we'll explore it

further and use sql to do some really

cool things with data

lastly let's talk about data

visualization you've learned that data

visualization is the graphical

representation of information

some examples include graphs maps and

tables

most people process visuals more easily

than words alone

that's why visualizations are so

important they help data analysts

communicate their insights to others

in an effective and compelling way when

you think about the data analysis

process

after data is prepared processed and

analyzed

the insights are visualized so it can be

understood and shared

this makes it easier for stakeholders to

draw conclusions

make decisions and come up with

strategies

some popular visualization tools are

tableau

and looker data analysts like using

tableau because

it helps them create visuals that are

very easy to understand

this means that even non-technical users

can get the information they need

looker is also popular with data

analysts because it gives them

an easy way to create visuals based on

the results of a query

with looker you can give stakeholders a

complete picture

of your work by showing them

visualization data

and the actual data related to it all

visualization

tools have great features that are

useful in different situations

soon you'll learn how to decide which

tool to use for a particular job

and that's everything you need to know

about the data life cycle

and the data analysis process you'll get

a chance to test out

what you know so you can feel confident

moving forward

in this course feel free to take some

time to re-familiarize yourself with the

concepts

and when you're ready give it your best

shot if you're ever unsure of an answer

you can always go back and review the

videos and readings

then you'll be ready to move on to the

next set of videos where we'll continue

exploring the data analytics tools

you've already

covered and you'll get some really

fascinating insights

into exactly how they work before long

you'll have the knowledge and confidence

to start using them yourself stay tuned

welcome back in the next few videos

you'll continue to explore the data

analytics tools

we discussed earlier and you'll get the

chance to see them

in action a little bit this will give

you a clearer picture of how to use

these tools

the rest of the program will build on

from what you learn here

we'll start with a closer look at

spreadsheets

we'll break spreadsheets down to their

basics to better understand

a few of their features and functions

you'll also learn how you might want to

use them

in your work as a data analyst for

example

how do you sort your data to make it

easier to use

we'll find out next we'll see sql in

action

data analysts use sql in their work all

the time

like when they need a large amount of

data in seconds

to help answer a quick business question

chances are

you're not familiar with sql that's okay

you'll learn how using sql is just like

ordering food

at a super speedy restaurant your sql

query

might not be as delicious but you won't

have to wait long

to get your order speaking of food

what better topic than dessert you can

think of data visualization

as the dessert to the meal of data

analytics

it's served at the end of your analysis

after you've done what you need to get

the right data

for a question or task we've already

seen that visualizations

come in a lot of forms like graphs or

charts

and just like dessert they're a treat to

look at

you'll learn more about these visual

representations and see other examples

of how they might look then you'll get

to talk about visualizations

with other future data analysts just

like yourself

we'll wrap things up with an assessment

but you'll have time to review

what you've learned before then okay

let's keep going by the way is anyone

else hungry now

on october 17 2019 we celebrated the

40th anniversary of a very special

event well special for people like me

anyway

in 1979 visit calc was introduced to the

world

as the first computer spreadsheet

program while spreadsheets have changed

a lot since then

it was still an important achievement

and so now

we celebrate october 17th every year as

spreadsheet day

while there's a good chance you've never

been to a spreadsheet day party

spreadsheets are a big part of data

analytics

the sooner you make friends with

spreadsheets the better

trust me they'll save you a lot of time

as a data analyst

and make your job easier this

spreadsheet

is one example of how an organized

spreadsheet might look

in this video we'll demonstrate some

basic spreadsheet concepts

for all of you who are new to this world

this might be a review for some of you

more experienced folks out there

but it never hurts to practice what you

know

plus you might still learn a new trick

or two

i showed you this image earlier let's

explore it further

because it's a great example of the

three main features of a spreadsheet

cells rows and columns they'll be a part

of almost

everything you do in a spreadsheet from

making a simple grocery list

to analyzing a complex data set i use

spreadsheets to manage everything from

my own personal finances

to the annual homecoming party my

friends and i have every year

i'm the planner so i use a spreadsheet

to keep things in order

making sure we have everything we need

speaking of keeping things in order

the columns in a spreadsheet are ordered

by letter

and the rows are ordered by number so

when you talk about a specific cell

you name it by combining the column

letter

and row number where the cell is located

for example

in this spreadsheet the word row is in

cell d3

pretty simple right let's get started in

an actual spreadsheet

you can complete all of these steps in

just about any spreadsheet program

let's get to know your spreadsheet a

little better now

alright we'll start with some basic

operations

keep in mind as an analyst you won't

always create your own data sets

but for now let's do just that i'll

click in cell

a2 and type my first name in the cell

like this next i'll click in cell

b2 and type my last name don't worry if

your name doesn't fit in the cell

you can always make the columns wider if

you need to

all you have to do is click and drag the

right edge of the column

until the name fits or you can also use

the text wrapping feature

which will set cells to automatically

change their height

and allow the text in the cell to fit to

use this feature

select the cells columns or rows with

text

then use the format menu to look at the

text wrapping options

it is automatically set to allow the

text to

overflow out of the cell but you can

wrap the text instead

so all of the text is visible the clip

option

will cut off the text in the cell so

only the text

that fits is visible there it is

we've added data now let's label the

data

this is important for organization

adding labels to the top of the columns

will make it easier to reference and

find data later on

when you're doing analysis the column

labels

are usually called attributes an

attribute is a characteristic or quality

of data used to label a column in a

table

you might hear them called variables or

a few other names too

all right let's add some attributes to

our data

i'll click in cell a1 and type the words

first name

in cell b1 i'll type last name

we'll make these attributes bold so they

stand out more

spreadsheets can be really big so you

want to make sure your data is clearly

labeled and easy to find

so let's make these attributes stand out

i can use my cursor

to select the cells with the attributes

then

i'll click the bold icon to make the

attributes bold

looking good so far ready to add some

more data

let's start with some new attributes

first i'll add a column

for age by typing age in cell

c1 then i'll add two more attributes

in the next two columns let's go with

favorite color

and favorite dessert i'll make them bold

too

and to fit the labels in the cells i'll

adjust the size of the columns

just like before now keep in mind there

are more ways to adjust

the size of columns and rows if you have

questions about using spreadsheets

a quick search online will usually help

you find what you need

we've also included a reading with more

tips and information

about spreadsheets okay

let's get back to it now i can add my

own data to the data set

i'll type in my age and favorite color

and dessert

in the appropriate cells

next i'll add data for two more people

we now have three rows of data in a data

set a row is called

an observation an observation includes

all of the attributes for something

contained in a row of a data table

in this case row 3 is an observation

of willa stein because we see all of her

attributes in this row

so now we know spreadsheets let you do

lots of things with data

you can store and organize data like

we've done in this spreadsheet

but you can go even further and

reorganize existing

data too here i'll show you how let's

say we want to

organize our data by age there's a

simple way to do that

first we'll need to select all of our

columns with data

so that all of it gets reorganized

together

then we can go to our data menu here we

have some options

let's select sort range this will let us

choose

how to organize the column next we'll

choose

a to z which will organize our numbers

in order from the smallest to the

largest

now we want to watch out for our header

row

which is the word age the attribute

for this column we'll check that box

this makes sure that the word age stays

in place

all right now we're ready to sort

voila we just reorganized our data by

sorting it

from the smallest number to largest and

as we go further

you'll discover lots of other ways to

work with data in a spreadsheet

including functions and formulas

let's finish with a quick example of a

formula

you can think of formulas as one way of

manipulating data in a spreadsheet

formulas are like a calculator but more

powerful

a formula is a set of instructions that

performs a specific calculation

using the data in a spreadsheet to do

this

the formula uses cell references for the

values it's calculating

let me show you here we go we'll click

in the next cell

in the age column then we'll type an

equal sign all formulas begin

with this symbol next we type average

this is the function we are using in the

formula

we've briefly discussed how functions

work before

but it's okay if you don't completely

understand them yet

we'll take a closer look later on in

this case

we follow the function average with the

left

parentheses now we can add the names of

the cells

where we find the data we're using these

are the cell references

the formula will use to make its

calculation

we'll start at the top with cell c2

c2 represents the value in the cell in

this case 36

then we'll add a colon next to it which

shows

that we have a range of numbers in

consecutive cells

finally we complete our formula by

adding the last cell reference in the

range

c4 and a right parenthesis to end it

then we press enter to perform the

calculation

and there it is the formula has given us

the average age

of the ages in this data set we've just

analyzed some data we'll want to store

the data for later use

in google sheets a spreadsheet is

automatically saved

in your google drive for excel and other

spreadsheets

you'll save them as a file and now you

know

some basics for using spreadsheets once

you're used to these concepts

you'll be able to learn even more about

spreadsheet tools

it's a lot to digest so feel free to

re-watch and practice on your own

you can even make your own version of

this spreadsheet with your own data

we'll work together in a spreadsheet

soon as well

for now good job for sticking with me

through this

it'll be worth it as you might remember

earlier we touched on the query language

sql

in this video you'll see sql in action

and finally learn what you can do with

it by taking a look

at some examples of specific queries

i guess you can call this the sql sql

we'll try to make this one at least as

good as the first

remember sql can do lots of the same

things with data

that spreadsheets can you can use it to

store

organize and analyze your data among

other things

but like any good sql it is on a larger

scale

bigger more action-packed think of it as

super-sized spreadsheets

for example you might want to consider a

spreadsheet

when you have a smaller data set like

100 rows

but if your data seems to go on forever

and your spreadsheet

is struggling to keep up sql would be

the way to go

when you use sql you'll need a place

where the sql language

is understood if you've ever gone

somewhere

and not known the language it can be

challenging to communicate

you might think you're asking for one

thing and get something completely

different

well sql knows that feeling

sql needs a database that will

understand its

language so let's talk there are a

number of databases out there

that use sql you may use several of them

during your time as a data analyst but

here's the thing

no matter which database you use sql

basically works the same in each

for example in sql queries are universal

we've talked about queries before but it

never hurts to have a refresher

a query is the way we use sql to

communicate with the database

here's the structure of a basic query

you can see that with this query

we can select specific data from a table

by adding where we can filter the data

based on certain conditions all right

let's get started we'll open our

database and see how sql can communicate

with it

to do some simple data tasks

first let's select our data we'll use an

asterisk

to select all of the data from the table

and with that simple query

the database calls up the table we need

magic let's add where to the query to

show how that changes

with data we get

you can see that the data now only shows

movies

that are in the action genre and that's

it

a basic query in sql pretty cool huh

there are plenty of other commands that

you'll use in queries

as you continue for now though we can

celebrate learning about

three big ones select from

and where as you continue the program

you'll have the opportunity to use sql

yourself

so i hope that this video was a useful

sneak peek

at what's coming later like with any new

language

learning it takes time and now it's time

to move on

i'm angie i'm a program manager of

engineering at google

i'm currently working on the data

analytics certificate

and previously i was a researcher in

people analytics i was also

what i call an analytical mercenary

working for a lot of different companies

to help them make sense of their data

every time i learn

a new skill i feel like i'm learning how

to speak

all over again i remember the first time

i learned sql i was so frustrated

because

everyone around me just it felt like

they were fluent

they knew exactly what they were doing

and i remember

struggling with the most basic things

just like getting the data

out of the table right or i remember

somebody asked me just to find like an

average of something and i kept on

getting an error and it really does feel

like you're learning a new language

and you're at toddler level and everyone

around you is like maybe fluent

so my parents immigrated to this country

when they were in their 30s so after

you know they had learned another

language and they had to start over and

learn

you know english and i remember as a

child watching them

struggle every day to pick up a new

language to do really basic things

like ask for help at the grocery store i

remember

calling the cable company when i was six

asking them

questions about the bill because my

parents couldn't and

i remember how hard they worked to learn

this new language

and to become fluent you know and every

time i'm learning a new data language

like sql or r i think about how hard

that must have been

and i i think if they can do that

i can learn sql um if they can ask for

help for the most basic of things

i can ask the data analyst next to me

you know how to write a sql statement

how to get data out of a table

and that's really helped me is just

having that mindset and knowing that i

can ask for help

wow your data analysis toolbox is

getting full

learning about both spreadsheets and sql

will get you

far in the world of data analysis

there's more to learn of course

and lots more tools you'll be able to

use but your future is looking bright

and it's about to look even brighter

because we're here to talk more about

data visualization

i'll tell you a little more about the

role of data visualization tools

in data analytics and give you a chance

to see

those tools in action later in this

video

you might remember that data

visualization is the graphical

representation of information

for tons of data analysts it's the most

exciting part of their job

because they get to see their hard work

pay off with something interesting

not to mention that data visualization

is beautiful

and useful i was floored when i got to

google

and started to get a quarterly data

report in my email

it had a big slide deck where people

contributed their

visualizations it was definitely a

source of light

as i started to build my own

visualizations

if you're not impressed by my story let

me tell you about

florence nightingale does that name ring

a bell

she's responsible for much of the

philosophy of modern nursing

and believe it or not she was also a

data analyst

during the crimean war in the 1850s

thousands of soldiers were dying every

day

nightingale wanted to find a way to

reduce the number of deaths

after examining the data she found that

the majority of soldiers

were dying from preventable conditions

to convince hospital administrators

that they needed to focus on these

conditions she created a chart

showing the number of deaths over

several months the much

larger blue sections individualization

represent

the preventable deaths her work directly

led

to major changes in patient care and she

did all of this

over 150 years ago without a computer

one of the main reasons nightingale

created this visualization

was to make the data easier to digest

for her audience

she felt she'd be more successful

convincing the stakeholders using

visuals

instead of just words and numbers she

was right

tables filled with data while necessary

for analysis

just aren't able to show trends and

patterns as quickly

and clearly as visualizations can

imagine you receive an assignment that

needs to be completed the same day

you gather the data you need in a table

could you explain

your findings using the table yes you

probably could

but a better idea would be to use a

visualization

like this bar graph something like this

makes it much easier for you to explain

quickly

and you've got the benefit of a cool

graphic to back up your analysis

as a data analyst you'll want to create

visualizations

that make the data easy to understand

and interesting

to look at so show it off stakeholders

may not have

much time to devote to the data your job

will be to make their time

worthwhile let's go back to that data

table we created

earlier in the course if you created

your own for practice

you can open it up now or try this out

later

here's the data we added before let's

create a visualization

of the data by inserting a chart a bar

graph

boom you can see that the spreadsheet

visualized the data from our table in a

way

that made the most sense it created a

bar graph

or column chart to compare the ages of

each person by name

but you might have figured that out

already that's the beauty

of visualization it shows data analysis

quickly and clearly we can use chart

editor

to adjust the chart different

spreadsheet programs might have

different ways to do this

but they all have visualization

functions and ways

to edit those visualizations alright for

now

let's just look at the suggested charts

we can make the bars go horizontally

using a bar chart that looks great

so let's close the chart editor

there are lots of options to look at but

we'll keep it basic for now

feel free to try other visualizations if

you practice

later now we can adjust our chart to

make our whole spreadsheet

look clean and professional

excellent i hope you learn to love data

visualization

as much as i do maybe you'll become a

data visualization pioneer just like

florence nightingale

as a budding data analyst you've started

to feel

your utility built with valuable tools

that you'll use

throughout the rest of the program

having spreadsheets

sql and data visualization know-how

will help make you an ace data detective

you'll be able to use these tools

throughout the data analytics process

as you move forward coming up next

you'll complete a few activities to wrap

up this part of the program

you'll also complete an assessment to

check your understanding

of all that you learn this is a great

opportunity

to think about some of the areas that

you'll continue to explore

in this course and in your career

as always feel free to review the videos

and readings

to help remind you of certain topics and

ideas

even if you already feel prepared you're

just a few steps away from the next

course

that's great progress keep it up

my name is lyla jones and i am a part of

our cloud team

i get a chance to lead a team of amazing

individuals that are focused on helping

customers get to the cloud

data visualizations that's a long word

and that can also make your eyes glaze

over but i wonder

if when you were little and you were

with your parents maybe they had a

bedtime routine or maybe you have

children you're doing a bedtime routine

with them

you very rarely are going to come to

those children with a bunch

of facts and figures before they go to

bed but i bet you probably are telling

them a story

you're showing them pictures i know i

always loved comic books

pictures tell a story data

visualizations

are pictures they are a wonderful way to

take very basic ideas around data

and data points and make them come alive

you can do all different types of

combinations of visualizations but the

ones that are interactive

wow those are huge can you imagine being

an executive in organization

and trying to figure out wow should we

open up another

site in in bangkok does that make sense

and us being able to walk in and saying

here's why it makes sense and having

great data visualizations to support all

of our points of view

makes it a no-brainer interestingly

enough i do recall the first time i came

across

a super amazing visualization it was in

my personal life

i switched my budgeting software from

one provider to another

and the provider that i switched to was

really focused on

every dollar has a job and making sure

you're budgeting every single dollar

they gave visualizations that change

depending on what input you would add to

it

and it really just changed my entire

perspective the entire thing

so having the data is like having the

answer sheet for a test

it really just lets you know that you're

going to make good decisions because

it's backed up by data

hi in this video we're gonna be taking a

look how you can access quick labs

from within your coursera user interface

let's take a look

here we are and you're working your way

through a course and you see

inside of your left side bar a graded

external tool

now first of all you should get excited

that's where you're actually going to

apply a lot of these concepts that you

learned

hands-on inside of our quick labs but

how do you get from corsair to quick

labs

it's pretty easy it'll take us just

about a minute so what you're gonna do

you land on this page we have some tips

and tricks here for you and i'll walk

you through one key important one

the number one thing that i normally get

wrong when i'm recording these videos or

taking coursera courses with quick labs

myself

is that you want to make sure that

you're in a private browsing or an

incognito mode window

inside of your browser why would you

want to do that

well if you're logged into corsair with

incognito and keep in mind you might

have to go through a captcha

by selecting some images to make sure

that you're not a robot before you go

through it allows you not to

accidentally make the mistake

of logging into quick labs or coursera

with any email that you don't intend to

so once you're here and making sure that

you're in an incognito window and if

you're using chrome in the upper right

hand corner you can just verify there's

this little incognito icon here then

you're good to go

all you got to do is scroll down to the

bottom

click the i understand box add your

initials

and the most important thing that you're

going to see is open tool

in the lower right hand corner this

button will become a nice dark blue once

you've checked that button

you open the tool and you'll see a new

browser window come up

now unless you want to hear my voice

again you can just click skip this video

because that's just an introduction

there's also an x in the upper right

hand corner for you

which will then take you into the quick

lab itself so to do your work you should

now have

two different tabs in your browser one

has all the video lectures inside of

coursera

and the other has the actual hands-on

quick lab and that's gonna be the

subject of our next video

where you learn all about how you can

get credit for your work done inside the

quick labs

we'll see you there

welcome to quick labs now it's time to

get hands-on practice

and a lot of the amazing data analyst

concepts that you've learned so far

here's where you get to prove that

you've learned some great technologies

and then you'll get credit for it back

inside of coursera but before you jump

right in

let me just give you a quick walkthrough

of what a quick lab is

and then we'll get started first and

foremost in the upper left-hand corner

you'll notice a gigantic tempting button

called start lab in green

i want to go ahead and start that click

this box that says i'm not a robot

select a few of the different items for

your for capture you can see this is the

hardest part of the lab is trying to

actually get through the anti-robot

technology that they have here

now a couple of things happen in the

background as author of these labs

this is some of our best work that we're

getting you hands-on practice for

and what we're going to do next is just

make sure that that timer starts

counting down

because then that's going to give you

the account logins that you need to

practice the work inside the lab

now here's what you're gonna do there's

gonna be a gigantic big button that says

open google cloud console i want you to

go ahead and click on that

and now you have three browser tab

windows open or a hundred if you're like

me

and in the second browser this is your

quick labs login

all the way under username what i want

you to do is copy that username and

here's the number one mistake that i've

seen students make

when you're back into google everyone

sees this google sign in screen here

and they immediately start typing in

their personal gmail address you want to

be charged for any of the resources that

you can be using

that's why we're providing you with this

quick lapse account yourself

all i'm going to do is paste in the

email that's been given to me by the

quick lab

for a use for an hour and then click

next

and then i'm going to go ahead and grab

the password

paste that in here

and then you're going to see a bunch of

terms and conditions because this is a

brand new

account scroll through the terms and

conditions read them at your leisure

click accept and as you work through the

terms of service here

eventually you'll land on the homepage

for

google cloud scroll down read the terms

of service

accept terms of service all the steps

that i'm walking you through right now

are also available inside of the lab

written instructions as well

so once you've gotten that out of the

way let's take a look at what this

particular quick lab is going to teach

you

at a high level i'm not going to do the

lab for you but don't worry it's step by

step you'll have a blast doing it

and you have way more than enough time

you need in this timer before your lab

timer runs out

generally as lab authors we try to

double the budget of allowed time

so don't worry about lab timeouts so

inside the lab

every lab will start with an overview

and then what specifically it is that

you're going to do

inside this lab since you're a data

analyst you'll be creating spreadsheets

and adding files and sharing files with

some of your other data analysts

if you forget what section you're in

another pro tip that i like to do is

on the right hand side you can actually

click between each of the different

header sections right here as i'm doing

now

and you can jump to a particular section

inside of the workbook so say you and

your friends are working on this

together

you can say hey i'm having trouble on

the share and collaborate section

you can just click on that and it'll

jump right to that section

well that's pretty much it a few more

housekeeping items and then you're off

to the races

as you work your way through the lab you

want to make sure that you're completing

the objectives

and making sure that you're staying on

track there is intelligence built into

quick labs

to prevent any kind of behavior that's

not part of the lab so make sure you're

following the lab as is

and when you're done with the lab say i

was completed here

all you have to do is click on end lab

click on ok

and that's going to bring you to a

pop-up screen that asks you to rate the

lab

add in any comments for the lab authors

like me

submit it and then automatically your

score for the work that you've done in

the lab

is fed back to coursera which is pretty

cool

and that's it that was a quick tour of a

quick lab is about five minutes

but honestly you'll have a blast going

through these good luck

[Music]

next we're going to cover a really

important topic which is how to get help

with quick lab should you need it

and to do that we're going to jump back

into our quick labs user interface

here i am back inside of the quick lab

inside the quick labs user interface

if you're working through a lab and just

something's not acting right or

the lab instructions don't seem to be

telling you exactly what you need to do

we try our hardest to make sure that the

labs are step by step but at any point

in the lower right-hand corner you can

click on the chat bubble once we've done

that

add in your name add in your email and

you can

optionally define the department for

quick labs

i'm going to say quick lab general

support and you can start a chat so let

me go ahead and add my name

add my email

and start chat and then this window will

pop up

now this gets you connected with the

quick labs live support representative

they're amazing they're friendly they've

been doing this for years and likely

your problem has already been

encountered by somebody before

so don't hesitate drop them a line and

if they're out of office

it'll automatically create a support

ticket where they'll follow up with you

from that email address that you

provided that's it have fun working

through your labs and earning that

credit

hey great to have you back now it's time

to get down to business

we're going to start talking about

practical ways businesses

are using data and the opportunities

that it can create for you

so far you've learned a lot of practical

data analysis skills

with these next couple of videos we're

going to switch gears a little

and talk about why you're learning these

skills hopefully this will give you more

perspective into what kinds of

opportunities are out there for you

coming up we're going to talk more about

the kinds of roles data analysts play in

different industries

the tasks that these roles require and

the importance of fairness and avoiding

bias

and analyzing data for business tasks

we'll also talk about opportunities

you can tap into and how this program

factors into your future success

in the data analyst role so with all

that in mind let's get started

previously we learned about what a data

analyst does

and why that work is so valuable now

let's look at

where data analysts actually do their

work you'll learn much more about the

industries

you could work in as a data analyst and

how companies in these fields are

already using data analytics

to do some really cool things there are

so many businesses out there

that have a big need for the skills

you're learning right now

across industries like technology

marketing finance

health care and so many more real

companies are already using data

analytics

to stay ahead of the curve and the more

they use data in their business

the more they understand just how

important data analysts

like you are to their success let's look

at a real life example

of a brand you'll probably recognize

coca-cola

data is changing the way coca-cola

approaches its marketing strategies

coca-cola uses data gathered from

consumer feedback

to create advertising that speaks

directly to different audiences

with different interests how does this

work you know those high-tech coca-cola

vending machines

you see at movie theater sometimes it's

always fun getting to make your own

flavors

well those machines have built-in

artificial intelligence

and data analysis tools this helps

coca-cola

see all the different kinds of flavor

combinations people are coming up with

which they can then use as inspiration

for new products

how cool is that ever wonder how google

gives you the right answer

to any question in just seconds that's

powered by data2

we use all kinds of data to determine a

website's reliability

and accuracy to make sure you get the

most useful results

for any search you make but it isn't

just big companies like coca-cola

and google that use data small

businesses

everywhere are also starting to take

advantage of data-driven insights

to improve their operations and make

better decisions

small businesses can use data to do all

kinds of things

they might use data analytics to better

understand their customers buying habits

create more effective social media

messaging or

in the case of one city zoo and aquarium

predict the number of daily visitors

based on local climate data city zoo and

aquarium realized that

on rainy days they were seeing huge

drop-offs in attendance

but they had no way to accurately

predict when those rainy days would hit

this made staffing a real challenge some

days they found themselves overstaffed

other days they were unprepared for the

rush of visitors

to deal with this data analysts took

years of weather records from the zoo

and used that data to accurately predict

future weather patterns

this made it easy for the zoo to know

how much staff they needed when

because the zoo could predict and manage

their staffing needs more accurately

they were able to provide a better

experience for visitors and dedicate

more resources

to creating a better experience for the

animals too

we see a similar thing in the health

care industry there

data analysts look at clinic attendance

data to help

hospitals and doctors offices predict

when rush hours will hit

so they can be ready for it your local

city hospital

is a great example let's say they've

been getting complaints about long wait

times

sometimes an hour or more which made it

hard for some patients to get the care

they needed

so data analysts use data about the

hospital's daily foot traffic

to help them make more informed

decisions about how many doctors they

need

on staff at any given time this helped

reduce wait times

improve their patients experience and

make better use

of the healthcare workers time too like

i said

there are many ways that companies in

different industries put data to use

but they can only do that if they have

data analysts

they can rely on so you might be

wondering how you fit into the equation

well you've got plenty of options but

you don't have to decide what industry

you want to work in

by the way there will be plenty of time

to think about that

as you make your way through this

program by the time you finish this

program

you'll have the core skills that will

make you valuable in any industry that

makes data driven decisions

which as it turns out is most industries

even zoos coming up we'll check out the

business

task where data can be helpful and we'll

explore even more

how data analysts are empowering

businesses through data

i'll see you then hi

i'm joey and i work as an analytics

program manager within ruse

now ruse stands for real estate and

workplace services

and my job is to bring data and

analytics

into the decision making here especially

with regards to

creating a safe and fun work environment

my journey into analytics was a bit

different in that i had no plan

or really didn't see myself being where

i am now

now luckily i started in a rotational

program called the hra program

within people operations which afforded

me the ability

to play three different roles

essentially i was

in a generalist capacity in a specialist

role and as an analyst and i really

found a love and a passion

in the analytical work i started on the

business intelligence team

whose job was to provide sql based

reporting back to the business

i realized that analytics is the right

career path for me

when i found myself enjoying coming to

work and getting my work done

and i think i can connect that to two

passions of mine

the first is problem solving i love

taking a complex

problem a mystery a riddle and being

able to

find the answers and come up with a

solution

and then the second thing is being able

to work with people and help people

in analytics i feel like the key to

success is being able to blend

the personal side with the technical

side

at the beginning of my career i focused

a little more on the technical pieces

and i wanted to make sure i had the

right technical knowledge to be able to

answer questions

but what i found is over time i needed

to grow that other side just as much

and i think that my career has allowed

me those opportunities

to kind of work each of those muscles

the human interaction part

and the technical part to make sure that

they're both growing

at the end of the day

for any analyst for any person that's

honestly at the early

stages of their career understanding

data respecting data and knowing how to

work with data is incredibly important

because

you know my vision is that every role in

some form or fashion will involve data

and it's used in learning how to extract

insights from it

will be at the core of any critical role

across any company organization

generally in those first two years

you're developing the core skill sets

that make you a fantastic generalist

and then in the next two to five years

you're learning about

something very specific as a as it

relates to your job so

whether it's the area that you're

supporting or maybe a very technical

component

like let's say you want to become a sql

expert so that you can manipulate large

data sets for financial analysis

purposes

similarly even if you come into finance

as a data analyst

you can pop out of finance and go into

what a lot of people like to call the

business

which is typically your operations

functions and become

a business analyst or a data analyst

there's so many different paths that you

can take from the starting point

that you really can't predict you're in

i'm just deeply passionate

about working with and supporting young

people and really giving them

a jump start to their career this stems

from honestly

my own personal experience where in the

first two years of my career

i had essentially zero support from my

manager and my direct management chains

having gone through that experience in

my first two years i

realized and i felt to experience how

that can slow you down

and especially when you're somebody that

has a lot of potential

and a lot of ability you want to be able

to be in an environment

that fosters that ability and really

wants to see you grow

i think it's incredibly important to

have programs like these

that take away all the barriers that

remove any of the constructs that

prevent people from being able to

find out what they need um to be

in an industry like this to be

successful in a role like a data analyst

so that they themselves can dream about

where they can go in their career

my name is tony i'm a finance program

manager at google

as a data analyst you'll be tackling

business tasks that help companies

use data coming up we'll talk more about

what a business task actually is and

some examples of what they might look

like in real data analyst jobs

let's take a second to think back on the

real examples of businesses using data

analytics

and their operation we've seen before

you might have noticed

a common theme across every example they

all have

issues to explore questions to answer or

problems to solve

it's easy for these things to get mixed

up so here's a way to keep them straight

when we talk about them in data

analytics an issue is a topic or subject

to investigate

a question is designed to discover

information

and a problem is an obstacle or

complication

that needs to be worked out coca-cola

had a question about new products

data analysis gave them insights into

new flavors customers

already like the city zoo and aquarium

had a problem with staffing data helped

them figure out the best

staffing strategy these questions and

problems

become the foundation for all kinds of

business tasks

that you'll help solve as a data analyst

a business task is the question or

problem

data analysis answers for business this

is where you'll focus a lot of your

efforts

in the work you'll do for future

employers let's stick with our zoo

example

and see if we can imagine what a

business task for a zoo might look like

we know the problem unpredictable

weather was making it hard for the zoo

to anticipate staffing needs

so maybe the business task could be

something like

analyze weather data from the last

decade to identify

predictable patterns the data analysts

could then plan out the best way

to gather analyze and present the data

needed to solve this task

and meet the zoo's goals then using data

the zoo would be able to make informed

decisions

about their daily staffing so we talked

a little about

data-driven decision-making in previous

videos

but just in case you need a refresher

here it is

data-driven decision-making is when

facts that have been discovered through

data

analysis are used to guide business

strategy

the simplest way to think about decision

making is that it's a choice between

consequences

good bad or a combination of both

in our zoo example the zoo had the data

they needed

to make an informed decision that solved

their problem

but what if they had made this decision

without data

let's say they just relied on

observation and memory to track the

weather

and make staffing schedules well we

already know that wouldn't have solved

their problem long term

data analytics gave them the information

they needed

to find the best possible solution to

their problem

that's the power of data observation and

intuition

are powerful tools in decision making

but they can only take us so far

when we make decisions based on just

observation and gut feelings

we're only seeing part of the picture

data helps us see the whole thing

with data we have a complete picture of

the problem

and its causes which lets us find new

and surprising solutions

we never would have been able to see

before data analytics

helps businesses make better decisions

and it all

starts with a business task and the

question it's trying to answer

with the skills you'll learn throughout

this program you'll be able to ask the

right questions

plan out the best way to gather and

analyze data and then present it

visually

to arm your team so they can make an

informed

data-driven decision and that makes you

critical to the success of any business

you work for

data is a powerful tool and with great

power comes

well you know the rest and you're doing

a super job

taking in all of this information up

next

we'll talk about your responsibility as

a data analyst to make sure you're

gathering

analyzing and presenting data in a way

that's fair

to the people being represented by that

data

hi my name is rachel and i'm the

business systems and analytics lead at

verily

there are a lot of different types of

problems that a data analyst can solve

i've been lucky enough over my career to

have

to have seen a lot of them and to take

in a lot of very different types of data

and help turn that into meaningful

answers i think one of the most

important things to remember about data

analytics

is that data is data i'm a finance data

analyst

and so my role at verily is to take all

of our financial information

all of the information of the money

we're spending and the money we're

making

and turn that into reports and insights

so that our business leads can

understand what we're doing

one of the most important things i've

done it fairly recently was help create

what's called a profit and loss

statement for each of our business units

and that means that in real time our

teams can see

what their budget is and how they're

spending against that budget

and what that does is that helps our

teams keep to that budget

by either increasing their revenue

streams so that they have more money to

play with

or pulling back their spending so that

they can

keep themselves within that budget and

all of that really helps keep us

on track as a company and making sure

that we're hitting our goals

i've found that data acts like a living

and breathing thing

when you have a ton of data points it

can be

overwhelming when you first sit down to

make sense of it

you have tons of columns tons of records

tons of different types of data

and finding a way to make sense of that

is really hard

and that's where the expertise of a data

analyst comes in it has been some of the

most frustrating moments of my career

but also some of the most rewarding work

i've ever done when it finally comes

together

the best advice i have for any data

analyst starting out is keep at it

if the angle you're taking doesn't work

try to find another one try to come at

it in a different way

try to ask a different question

eventually the data will yield

and you'll get the insights you're

looking for

so far we've covered the different roles

data analysts play

in business environments and the kinds

of tasks that come with those roles

but data analysts have another important

responsibility

making sure their analyses are fair now

i know what you're probably thinking

data is based on collected facts

how can it be unfair well that's a good

question

so let's learn what fairness means when

we talk about data analysis

and why it's important for you as an

analyst to keep in mind

fairness means ensuring that your

analysis doesn't create or reinforce

bias in other words as a data analyst

you want to help create systems that are

fair and inclusive

to everyone sounds simple enough

well here's the tough part about

fairness and data analytics

there isn't one standard definition of

it but hopefully the way

we've just described it can give you one

way to think about fairness

for right now but it's about to get a

bit trickier

sometimes conclusions based on data can

be true and unfair

what can you do then well let's find out

with an example

let's say we have a company that's

notorious for being kind of a boys club

it's very male dominated and there

aren't many women employees

this company wants to see which

employees are doing well so they start

gathering data on employee performance

and their own company culture

the data shows that women just aren't

succeeding as often as men in their

company

their conclusion that they should hire

fewer women

after all women are doing poorly here

right

but that's not a fair conclusion for a

couple of reasons

first it doesn't even consider all of

the available data

on company culture so it paints an

incomplete picture

second it doesn't think about the other

surrounding factors

that impact the data or in other words

the conclusion doesn't consider the

difficulties

women have trying to navigate a toxic

work environment

if the company only looks at this

conclusion they won't acknowledge

and address how harmful their culture is

and

they won't understand why women are set

up to fail within it

that's why it's important to keep

fairness in mind when analyzing data

the conclusion that women aren't

succeeding in this company is true

but it ignores the other systemic

factors

that are contributing to this problem

but don't worry

there's a way to make a fair conclusion

here an ethical data analyst

could look at the data gathered and

conclude that the company culture is

preventing women from succeeding

and the company needs to address these

problems to boost performance

see how this conclusion paints a much

more complete and

fair picture it recognizes the fact

that women aren't doing as well in this

company

and factors in why that could be

instead of discriminating against women

applicants in the future

as a data analyst it's your

responsibility to make sure your

analysis is fair

and factors in the complicated social

context

that could create bias in your

conclusions

it's important to think about fairness

from the moment you start collecting

data

for a business task to the time you

present your conclusions

to your stakeholders we'll learn more

about bias

in the data analysis process later on in

another course

for now let's check out an example of a

data analysis

that does a good job of considering

fairness in its conclusion

a team of harvard data scientists were

developing a mobile platform

to track patients at risk of

cardiovascular disease in an

area of the united states called the

stroke belt

it's important to call out that there

were a variety of reasons

people living in this area might be more

at risk

with that in mind these data scientists

recognized that

fairness needed to be a priority for

this project

so they built fairness into their models

the team took

several fairness measures to make sure

they were being as fair

as possible when examining sensitive and

potentially

biased data first they teamed analysts

with social scientists who could provide

insights on human bias

and the social context that created them

they also collected self-reported data

in a separate system

to avoid the potential for racial bias

which might skew the results of their

study

and unfairly represent patients and to

make sure

their sample population was

representative they oversampled

non-dominant groups to ensure their

motto was including them

it's clear that the team made fairness a

top priority every step of the way

this helped them collect data and create

conclusions that didn't negatively

impact the communities they were

studying hopefully these examples have

given you a better idea of what fairness

means

in data analysis but we're going to keep

building on your understanding of

fairness

throughout this program and you'll get

to practice with some activities

hi i'm alex i'm a research scientist at

google

my team is called the ethical ai team

and we're a group of folks that really

are concerned not only about

how ai and the technology operates but

how it interacts with society

and how it might help or harm

marginalized communities so when we talk

about data ethics we think about

you know what is the good and right way

of using data

what are going to be ways that are going

to be uses of data that are going to be

beneficial to people

when it comes to data ethics it's not

just about minimizing harm but it's

actually this

this concept of beneficence how do we

actually

improve the lives of people by using

data

when we think about data ethics we're

thinking about

who's collecting the data why are they

collecting it how are they collecting it

and what for what purpose

because of the way that organizations

have imperatives to

make money or to report to somebody or

provide some kind of analysis we also

have to keep

strongly in mind how this is actually

going to benefit people

at the end of the day are the people

represented in this data

going to be benefited by this and i

think that's the thing

you never want to lose sight of as a

data scientist or a data analyst

i think aspiring data analysts need to

keep in mind that

a lot of the data that you're going to

encounter is data that comes from people

so at the end of the day data are people

and you want to have a responsibility

to those people that are represented in

those data second is thinking about

how to keep aspects of their data

protected and private

we don't want to go through our practice

thinking about data instances as

something we could just

throw on the web no there needs to be

considerations about how to keep

that information and likenesses like

their images or their voices or their

or their text how do we keep that

private we also

need to think about how

we can have mechanisms of giving users

and giving consumers more control

over their data it's not going to be

sufficient just to say

we collect elect all this data and trust

us

uh with all these data but we need to

ensure that

there's actionable ways in which people

can consent to giving those data

and ways that they can ask for it to be

revoked or removed

so data is growing and at the same time

we need to empower people to have

control over their own data

the future is that data is always

growing we haven't seen any kind of

evidence that data is actually shrinking

and with the knowledge that data is

growing these

issues become more and more peaked and

more and more

important to think about

now we know that there are all kinds of

jobs in different industries

available for data analysts but now it's

time to think about something just as

important

how can you tell if a job is a good fit

for you

and your career goals tough one right

don't worry that's exactly what we'll

cover in this video

there's a lot of important factors to

think about when searching for your

dream job

let's talk about some of the most common

factors first

industry tools location

travel and culture data is already being

used by countless industries

in all kinds of different ways tech

marketing

finance health care the list goes on

but one thing that's important to keep

in mind every industry has specific data

needs

that have to be addressed differently by

their data analysts

the same revenue data can be used in

three different ways

by data analysts in three different

industries financial services

telecom and tech for example

a finance analyst at a bank post public

revenue data of telecom company x

to create a forecast that predicts where

revenues will be in the future

to recommend the stock price the

business analyst at telecom company x

uses that same data to advise the sales

team

then a data analyst at the company who

created a customer management tool

for telecom company x will use that

revenue data

to determine how efficiently their

software is performing

finance telecom and tech

all use data differently so they need

analysts

who have different skills it all comes

down to what the needs of the industry

are

those needs will determine what kind of

task

you'll be given the questions you'll be

answering and even how you'll approach

job searching

if you're just starting out a great way

to guide your search

is to think first about what you're

interested in

does helping people get healthier sound

meaningful to you

maybe you want to focus on using data to

improve hospital admissions

what about helping people save for a

happy retirement

you might want a job that uses data to

determine risk factors and financial

investments

or maybe you're interested in helping

journalism grow in your city

a job using data to help find your local

news website

find more subscribers could be the

perfect role for you

the key is to think about your interest

early in your job search

that will lead you in the right

direction and it it'll help you in

interviews too

potential employers will want to know

why you're interested in their company

and how you can address their needs so

if you can speak about your motivation

to work in data analytics during

interviews you'll make yourself stand

out in a great way

you'll have options when it comes to

where you work

and who you work for but remember

you want to enjoy what you do so it's a

good idea to think about

how you want to use your skills then

search for jobs

that allow you to do that next on the

list of things to think about

location and travel when you start your

job search

you need to make some decisions about

where you want to live so it helps to

ask yourself some questions

does your preferred industry have

opportunities in your area

are you trying to stay local or would

you be happy relocating

how long are you willing to commute to

work every day

will you drive to work walk take public

transport

is that possible year round how do you

feel about working remotely

does working from home excite you or

bore you

and of course you'll want to consider

cost of living

and whether or not you want the

convenience of city living or a quiet

suburban home

and it's not just about where you'll be

based some jobs may ask you to travel

which could be an exciting chance to see

the world or a deal breaker

it's all about what you want out of this

job so start asking yourself some of

these questions

figuring out the answers can help you

narrow down your search even further

so you're only looking at jobs you'd

actually accept

once you've answered enough questions

you'll be able to identify some specific

companies

that fit your needs at this point it's a

good time to think about your values

and what kind of company culture is a

good fit for you

ready here comes some more questions

do you work best in a team or by

yourself

do you like to have a set routine or do

you enjoy

taking a new project and trying new

things

do your values match the company's

values you'll want to pay attention to

these things during your job search

and interview process so you can be sure

you fully

invested in the company you work for

that's the best way to start building an

exciting

and fulfilling career this program will

help you learn the core skills for data

analytics in

any setting it's up to you where you

want to take them

whether that means starting in a

completely new industry

or moving into an analyst position in an

industry you already have experience in

and hopefully what we've covered here

has helped you get on track for your

future job

search after this you'll have a few

activities to do

and then you'll be able to move on to

the next part of this course

we learned a lot so far like what kinds

of opportunities are out there for data

analysts

in different industries how data

analysts help businesses

make better decisions the importance of

fairness and data analytics

and the potential questions you can

start asking yourself

before your future job search and you

can always look back at these lessons

if you want to review in an upcoming

course

we'll look at the skills all successful

data analysts have

and you'll learn how you can start

practicing them too

but before that you'll have an

assessment good luck

and i'll see you later

my name is sama moid and i'm a recruiter

here at google for the large customer

sales team

basically i hire talent for the sales

team here even within the sales

recruiting space

i recruit specifically for the

analytical lead

roles here at google i want the

candidate to be as comfortable as

possible

as a recruiter i'm also their advocate

if they're a good fit for the team i'd

like to present them in the best light

as a recruiter some advice i would give

for

a data analyst that's just starting to

look for a job

think about a time where you've used

data to solve a problem whether it's in

your professional

or personal projects another tip i would

say for

a data analyst that's just looking for a

new a new job is to increase your

professional network

there are many ways to increase your

professional network one of them is to

increase your online footprint reach out

to other analysts on linkedin

join local meetups with other data

scientists sometimes when we're looking

for a really

a unique skill set recruiters are going

on websites like linkedin and github and

trying to find that talent themselves

it's really important to have your

linkedin updated along with

websites like github where you can

showcase a lot of the data analyst

projects you've done

another tip i would say for an in-person

interview is to prepare questions for

the interviewer

make sure they're not broad questions

they should be questions that will help

you

understand the team and and the job

better

if you're given a case study in an

interview you should expect to be given

a business problem along with a sample

data set

then you'd be asked to take that sample

data set analyze it

and come up with a solution one of the

things you can do to help prepare

yourself for this

is to ensure you are analyzing the data

and coming up with a solution that

relates back to that data

sometimes there is no right answer and a

lot of times interviewers are looking to

see your thought process and the way you

get to your solution

i highly encourage that if you find a

role that you're interested in not only

apply to it but go the next step look

for the recruiter look for the hiring

manager online see if you can reach out

to them

and set up a coffee chat or send them

your resume directly

online applications could be a really

big

black hole where you never hear back

from the recruiter or the team

when you reach out directly to a hiring

manager or recruiter it really shows

your

eagerness uh for the role and your

interest for the role

even if sometimes you don't get a

response from that from reaching out you

never know it's

you know you try multiple different

times and that one time you get a

response back from a recruiter or hiring

manager

could be the time you get the job that

you really wanted

we're at the end of this course which

means it's time to show off what you've

learned

we've covered the different kinds of

industries using data to drive

decisions and how you can help them how

to promote fairness in your data work

and opportunities that are out there in

the world of data

analytics i know you've got this and

once you finish the course challenge

i'll be right here to introduce you to

the next course

congratulations on finishing this first

course

you've already learned a lot and you're

ready to take

what you've learned and move forward and

if you ever need a refresher

just remember that these videos will

still be here whenever you need them

you might remember your next instructor

from her introduction at the beginning

of the intro course

get ready to meet my fellow googler and

your instructor

for the next course ximena she's ready

to help you get started on your next

step

towards finishing this program and

becoming a data analyst

this next course will build directly on

some of the topics

that you've learned so far and give you

insight into the things

we've already talked about like any good

detective

you learn how to ask the right questions

and use data

to find answers employees in every

industry

need to become comfortable asking

questions but this can especially be

true for data analysts

a lot of data analysts try to make their

work perfect the first time

even though they might not have all the

information

instead of asking questions they make

assumptions

that can lead to mistakes it's so much

better to be humble and inquisitive

and to ask questions i'll show you what

i mean

one of the analysts i supervised came

into google with no coding experience

he was nervous about leaving a great

first impression so he tried to study up

on multiple languages by himself

before he started when the work actually

began

he didn't ask us his team questions

or ask us for help when he ran into

roadblocks

there are a lot of great places to find

answers especially online

and his initiative helped him find some

of those places

but at the end of the day he forgot to

tap into his best

resource us his team

because he was nervous about how he

would be perceived if he asked us for

help

he almost missed out on some great

insights from his team members

as roadblocks persisted he realized he

needed to make a change

he stopped trying to guess expectations

processes and more

all on his own and started asking us

more questions

as soon as he embraced this new approach

he skyrocketed on our team

his learning went straight up the curve

like a hockey stick

his impact on the organization the

number of people who reach out to him

and his career path going forward all

did the same

the bottom line is you don't need to

know it all

the saying is true there are no bad

questions

being open to learning is one of the

most important qualities

for a data analyst speaking of learning

in the next course we'll go into more

depth learning about basic spreadsheet

skills

and when you'll need to use them you'll

discover how to apply structured

thinking to data work

and you'll focus on how to best meet

stakeholder needs

and expectations by gathering all the

clues

great work and good luck on the next

course