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 analyst 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 analyst 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 data sets.
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 are 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 makes 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 and 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.
Insight managers or analytics team managers often supervise the analytical
strategy of the team or of 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.
Compared with many other professions, the data career space is relatively
young. This application of data-driven work in organizations has grown
exponentially in the last several decades, which means there are many
different opportunities and much job security for you in the future.
Now that organizations have the technical capacity to take on their own
data focused work, they're looking for people like you with the right skills to
fill these jobs. Traditionally, companies have filled jobs in the data
career space with those from computer computer engineering backgrounds or from
statistics. Increasingly, there's been a shift towards de-emphasizing
engineering and instead, promoting analytical skills.
These skills can be learned in different forums like the program that
you're currently enrolled in.
Let's look at a scenario. Let's say that an enthusiastic and enterprising
person, that's you, is starting a new position at a company as a data
professional. Your company is a recognized leader in its industry.
Its workforce spans the globe and you are its newest member.
It's your first day on the job and you are ready to start working.
During your orientation, your company grants you systems access and onboarding
documentation. You're starting to have a clearer picture of how information is
generally shared with employees.
You still have many questions about the responsibilities of the position.
Later, you watch a video from the quarterly review meeting led by a
company executive. Watching the presentation, you get insight into the
quarterly budget, recent client interactions and some general
information on an upcoming project.
You now have a broad understanding of the company.
At this point, you still lack details about your specific responsibilities.
During your first week, you're invited to a virtual meeting of the data
professionals involved on the project that you've been assigned to.
As each data professional outlines their job responsibilities, you take
note of the differences among them.
After each participant speaks, you begin to realize that not all data tasks
are universal and that many data professionals end up adapting to meet
the needs of the current project and the needs of the data.
When you're new to a job, I would discourage you from over specializing
immediately. Instead, taking on a variety of tasks within a project is a
great way for newer data professionals to continue developing their skillset.
As a member of a larger group of data professionals, you're able to observe
and learn from your team members.
Once the analytical process is complete, the results of the project
will need to be shared, allowing everyone in the organization to have
access to the information.
This includes building user-friendly interfaces and communicating the
findings to different departments.
Working for a large company means that there is a good chance that you will be
dealing with vast amounts of information.
This will require more work than a single data professional can reasonably
provide. Because of this, you might encounter scenarios where organizations
have created teams of data professionals.
Throughout the rest of this section, you'll take a closer look at how complex
organizations are incorporating data professionals through data teams and the
division of responsibilities within these teams.