All language subtitles for 01_what-data-professionals-do.en

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.