With so
much data flowing in every day, there is a huge need for skilled professionals
who can derive meaningful interpretations from this data. So much can be done
with this data at hand – analyzing, visualizing, modelling and predictions. Not
all of this can be performed by one individual. All of these require different
skills in the data and analytics industry. Data analyst, business analyst, data
engineer and data scientist, these job titles, to an outsider, might sound very
similar — all working with data and analyzing it. But in reality, these job
profiles are actually very different. Still, there is a lot of overlap that
exists in these fields and acquiring and mastering these required skill set
might help one enhance their job prospects and enter into a more challenging
role.
Often,
professionals who enter the analytics space as data analysts desire to move
into the role of a data scientist or data analyst. A data scientist’s job is
more challenging and rewarding, which has led to a huge surge in professionals
flocking to this field.
Role of a Data Scientist Vs Data Analyst
Some of
the core functions a data analyst performs include:
- Mining data from primary and secondary sources
- Interpreting this data to study its patterns
to solve business problems with the help of statistical tools
- Cleaning data to remove information that is
not useful
- Using the information deduced from the data to
provide reports that can help in business decisions
A data
scientist, on the other hand, has the following responsibilities:
- Building models to solve business problems as
per the needs of the business
- Creating algorithms and machine learning
methods to test the data
- Using various visualization methods
to present the data and different findings from it
- Syncing the information from the data,
deep-diving into it to provide ways to solve the business problem at hand
Data Analyst to Data Scientist. How to make the
transition?
Before
diving into the ways one can transition to a more challenging role of a data
scientist, it should be made clear that this is not an overnight process. Being
a data scientist requires a combination of different skills, including a solid
grip over mathematical and statistical concepts, a good hold over programming
languages, and, most importantly, understanding a particular business problem
and how to solve it through data analysis and prediction.
Here
are a few ways that can get you started to move from a data analyst to a data
scientist role:
Building up core domain knowledge
Before
even thinking about making the transition, one has to be very clear about what
a data scientist does and introspect what has to be done to fill the gaps that
are needed to make the transition and the skills the person has now. A data
scientist not only handles data but provides much deeper insights from it.
Other than gaining the right mathematical and statistical know-how, training
yourself to look at business problems with the mindset of a data scientist and
not just like a data analyst will be of great help. This means that while
looking into a problem, developing your critical thinking and analytical
skills, getting deep into the problem to be solved at hand, and coming up with
the right way to approach the solution will train you for the future.
Improving coding skills
A data
analyst might not have great coding skills but surely has to know it well. Data
scientists use tools like R and Python to derive interpretations from the
massive data sets they handle. As a data analyst, if you are not great at
coding or don’t know the common tools, it would be wise to start taking basic
courses on them and use them then in real-world applications.
Take introductory courses in
data visualization, ML, deep learning
Along
with learning certain tools, getting introduced into the world of machine
learning, deep learning, and decision trees would just add to one’s growth. Of
course, no one expects you to become a pro from the very start, but developing
interest and deploying such algorithms in projects will surely benefit you in
your career.
Sachin
Birla, who works as a data scientist at EY, says, “Typically, a data analyst
only works with tabular forms of data, but nowadays, we see a surge in image
and text data. For image and text data, traditional machine learning algorithms
fail, and new deep learning algorithms or models are getting popular. So, if
you are thinking of making the transition to data science, you should learn
machine learning as well as deep learning algorithms. Apart from that, you
should have good knowledge of databases, basic math, algebra, statistics
and Python programming. So, the combination of all given skills will make you a
good data scientist.”
Exploring
skills outside work
Taking
part in hackathons, Kaggle competitions and other contests will help
you boost your confidence and understand if you can really apply the concepts
in real-world scenarios. Even if you do not perform amazingly well initially,
keep pushing harder. More and more practice and participation will show effects
in the long run.
Learn
to develop a “data scientist mindset” at work
A great
way to develop this would be to learn from data scientists who work with you.
Try to brainstorm with them and also figure out how they approach problems.
Getting an idea about their thought process while building algorithms would
help you understand the nuances of the job and how to build your thinking
capabilities.
Always
stay updated
Data
science is an ever-evolving field. One must always keep learning and keep
updated to stay relevant here. A great roadmap for an aspiring data scientist
would be to follow data science leaders on social media like linkedin and
twitter, read about the latest research being done, connect with other data
scientists, and attend data science conferences to stay motivated in their
transition journey.
Source : analyticsindiamag.com
