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You can learn a lot about

a career by looking at job postings.

If you've searched for opportunities in the data space,

you may have noticed different

data-related job titles with

similar responsibilities or postings with

similar titles listing different responsibilities.

Here's an example. At one company,

the role of data analysts will focus on using

statistics and models to craft

insights that inform business decisions.

Another job with the same title

at a different company may focus on

optimizing the tools and products

that automate analytical processes.

One reason for these inconsistencies is that data tasks

and responsibilities are dependent on

an organization's data,

team structure, and how they make

use of insights and analytics.

As such, some organizations choose

to be very specific with responsibilities.

Others leave job tasks quite broad in scope.

That's why this program

refers to the field as a career space.

With that said, when you're comparing

positions that have similar titles,

I encourage you to classify them based on

the skills used in their day-to-day activities.

Two of the most common titles are

data analysts and data scientist.

These can cover a wide range of job responsibilities,

many of which you'll gain

experience with in this program.

Traditionally, a data scientist was expected to be

a three in one expert in data analytics,

statistics, and machine learning.

But not all employers use

these conventions when writing their job descriptions.

Generally, any role that includes analytics

expects candidates to be able to function

as technically skilled social scientists,

looking for patterns and identifying

trends within big datasets.

Also, they develop new inquiries and

questions as they uncover the stories inside their data.

Their hard work can help steer

a company's future actions and guide decision making.

They allow their organizations to keep

a finger on the pulse of what's going on in the business.

Interpreting and translating key information

into visualizations such as graphs and charts,

allowing every stakeholder to understand their findings.

At times, they may be tasked with creating computer code

and models to recognize

patterns in the data and make predictions.

When you investigate job postings,

you'll encounter other titles

with similar responsibilities.

For example, junior data scientist,

data scientist - entry level,

associate data scientist,

or data science associate.

All of these roles include a mix of

technical and strategic skills

to help others make informed decisions.

In your career, you might encounter

other professionals in roles that

use data and analytical skills.

Some of these may overlap

with the skills you will learn,

but these roles are specific to

certain tasks or our supervisory positions.

Let's take a look at a few.

Data scientists depend on systems

within their companies to collect,

organize, and convert raw data.

Designing and maintaining these processes are some of

the most important responsibilities of a data engineer.

Their goal is to make data

accessible so that it can be used for analysis.

They also ensure that

the company's data ecosystem is

healthy and produces reliable results.

These positions are highly technical and

typically deal with the infrastructure for data,

usually across an entire enterprise.

You also need to have the ability to get data

before it even make sense to talk about data analysis.

Most of the technical work leading up to the birthing

of the data may comfortably be called data engineering.

Everything done once some data have

arrived is data science.

Similar to how a data engineer

oversees the data infrastructure,

there are data roles that manage

all aspects of data analytics projects for a company.

Insights managers or

analytics team managers often supervise

the analytical strategy of

the team or the organization as a whole.

As a data analyst,

you will likely report to

someone working in this capacity.

They're often responsible for

managing multiple groups of customers and

stakeholders and they're often a hybrid

between the data scientist and the decision maker.

Since this combination of skills is rare,

these positions are often more difficult to fill.

This role can have other titles

like analytics team director,

head of data, or data science director.

You may encounter another job role

in your scan of job postings.

Business intelligence engineer, or business analyst.

This role is highly strategic,

focused on organizing information

and making it accessible.

BI analysts synthesize data, build dashboards,

and prepare reports to address specific needs

for a business or requests from leadership.

If you're interested in learning more about

business intelligence and its opportunities,

I encourage you to look into

the Google Business Intelligence Certificate.

Now that you have some idea of the roles

found within the data analytics career space,

we'll begin to take a closer look at how data

professionals function within their larger organizations.