So a lot of times people often mistake data
scientists for an oracle, thinking that we
can predict the future.
So predictive modeling is a very small part
of data science.
Data science is a very vast universe where
a lot of streams and a lot of areas of expertise
you can do.
Hi, my name is Sumit Dutta.
I am a data scientist at Walmart labs for
the past one year.
I have been working in the field of data science
for around 3-4 years now
Before I proceed, I would want you guys to
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I have been asked this a lot that data
science is all about tools.
So, data science is as less about tools as
computers are to computer science.
There is a lot of science and a lot of work
which goes before and after you actually come
to use the tools.
There is a lot of exploration of data, a lot
of evaluation metrics which you need to know
and also the background of what you are actually
using, it’s an essential element when you
come to data science.
So I would say data science is far away from
tools as you can imagine.
Another thing is that people often think that
the coding background is a must to become
a data scientist, so a lot of my colleagues
and very smart people which I have worked
with are not actually from a computer science
background, some of them are from electrical,
some of them are from industrial.
So it doesn’t really matter if you are from
a computer science background or you have
coding background because this is something
you can pick up while you are working on data
science and this wouldn’t really be as much
of a blocker if you are willing to learn and
if you have the knack of solving problems
I don’t think that this is actually any
hindrance to your progress when you are looking
to be a data scientist.
I have also been asked opposite things as
well that, can we become a data scientist
without actually indulging in coding ever,
again this is also not true because as I have
been talking that you are responsible for
taking your ideas and your solutions to production.
You need to know how to write production-ready
code once you are done solving the problem.
So yes, it’s neither here nor there.
Also a lot of times when I tell people that
I am a data scientist, a lot of people confuse
it with me being a data analyst, so there
is a thin line between both the spaces.
Analytics has a lot to do with inputs that
are going on the backend of the things, meanwhile
data science as a problem solving, a lot of
times we sort of contributing to what is going
in the front lines.
A lot of times, a lot of decisions and a lot
of important calls are made based on the work
we do.
So there is, analytics as space and data science
as space are pretty far apart I would say.
So a lot of times people often mistake data
scientists for an oracle, thinking that we
can predict the future.
So predictive modeling is a very small part
of data science.
Data science is a very vast universe where
a lot of streams and a lot of areas of expertise
you can do.
There is deep learning, there is language
processing.
You can use data science to translate one
language into another.
You can use data science to actually figure
out if two images are similar or what is happening
in a video stream.
People are building self-driving cars out
of it now.
So predictive modeling is not really all we
do.
It’s a very vast space and predictive modeling
is just a part of it.
So also a lot of time people tend to overlook
basic concepts like probability, statistics,
linear algebra.
They think that they can overlook this and
sort of get a hold of data science which not
really is true because if you have to have
a good understanding and a deep knowledge
of what data science model is doing, how it
is doing, how can you make it better and how
can you actually use it to solve the problem
which is presented to you, the knowledge of
these three concepts amongst others is very
important.
These are the fundamental building blocks
that you would require, the basic things which
you would require when you transitioning into
data science as well.
Talking about transitioning into data science,
what I would want to say to people who are
looking to transition is, first of all, you
should have a very good clarity of why you
want to transition into data science, what
excites you about data science.
So you can grasp whatever concepts are there
with equal enthusiasm and also never overlook
the basics which are been talked about.
A lot of people just see very cool problems
today which are being presented out there
and try to straight away jump to solving that
like image or deep learning but what I would
suggest to them is to start up from the basics.
Do not skip over that because if you do it
correctly you will actually get a better hang
of what you are trying to do and you will
be a much more versatile and much more well-equipped
data scientist.
