Hey Everyone!
Many of you have reached out to us to seek
advice on a career transition into the field
of data science, artificial intelligence,
machine learning, and data analytics.
In this video today, we bring you the best
career and interview advice from the real
from the real-life data scientists.
But before we get started, do not forget to
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to stay updated on the upcoming interviews
with real-life data practitioners.
If you want to start AI tomorrow there are
three things I would say that you should have.
One is figure out what kind of learning
do you like?
Do you like to learn from a book or do you
like watching videos?
I like watching videos.
When you’re watching videos are you taking
notes?
Those kinda things, figure out your learning
curve, how are you going to do that.
The second thing is, find more friends.
You can find people in the forums, you can
talk to them and see and have this kind of
a community where in you can go to; you can
go to data science meetups and meet people
who are also the same, following the same
paths, struggling to learn, etc.
Then the third thing is, you’re having people
who have already gone through this.
Have some mentors.
I think that really helps a lot.
Talking to them will make a lot more sense
to you; you would also know where you’re
going wrong and you can also say that this
is the path I want to learn.
There is going to be a lot of clarity which
you’re going to get.
And the fourth thing that I am going to say
is this – Do not get stuck in theory.
It has to be hands-on.
Unless and until you run your first model,
understand and run your first model, it’s
ok even if it’s a BlackBox, just run it.
Even if you don’t understand python, just
run it.
Download a notebook and just run it on a Google
Collab or whatever it is, but just run it.
It’s OK.
Be more hands-on.
Only then you’ll learn a lot more.
So, 3 things: Figure out the course, whatever
you want to do; have a support structure of
friends, forums, etc, have a couple of mentors
or a mentor who is going to help you out and
the fourth thing, Be Hands-on, do more projects.
So Prashant I would say that breaking into
data science is just equivalent to breaking
into software engineering for someone who
does not have that kind of background.
To split it down into atomic parts, I would
say that you need to be passionate about that
field, you need to get a stronghold of the
basics, basic technical skills that you require
for that field.
Apart from that you should probably choose
an industry in which you have an inherent
interest.
For example, if you’re interested in Finance,
you should look for roles in the financial
industry as a data scientist.
And apart from that, you should have a knack
of augmenting your knowledge regularly because
it is an ever-evolving field so every day
you have new research papers being published,
the amazing research that is happening in
the AI and the community.
And there a new tools that you get to use
for implementing your solutions.
So you should have that sort of curiosity
and that sort of drive-in you to learn something
new each day and keep augmenting your knowledge.
If you feel you identify with this kind of
a skill set you’re on the right path of
transitioning into data science.
The thing is right now the people still have
confusion that anyone can be a data scientist
or not.
So I will say anyone can be a data scientist.
Even I am mentoring one student, he has absolutely
no background of maths and coding and he is
doing fine, very good in the data science
track.
So, regarding how can a person start with
data science or thing.
So, the curriculum is one thing that you can
find and that you can do online or on some
good platform.
The thing is you have to find a good platform,
and a good mentor to do that, to guide you
like these are the topics, these are the things,
this is the track, this should be given to
you and if a person follows that religiously,
he is doing with good intent and learning
and is very much motivated towards the course,
he can be a data scientist and even he can
be a good data scientist.
A couple of advice to future aspirants of
data science.
Those are like this- Work on your critical
thinking aspect; try to think, think with
your data; ask these two questions – why
and so what?
At every juncture whenever you’re given
something, try to find a sense in it, reason
to yourself why I am doing this?
And if I am doing this, so what?
If we do get a solution, who will use the
solution?
And can we improve the solution in some fashion?
The solution that I have in mind, can it be
improved so that it is more useful to the
end-users.
Second is, master the course structure.
You’ll learn data science as you do in the
field.
Everyday something new is coming to the field.
So you continue learning and there will be
no end to it.
But there are certain basic structures, certain
basic foundations, that you should have and
every hiring manager will want to look for
those basic things in you.
That is your basic maths, your basic programming
and there part of your Springboard curriculum.
Please master those things.
And third I would say – Have Patience!
Even if you don’t make in 1-2 interviews,
keep giving interviews, and definitely, you’ll
be there.
One of the biggest learnings which I have
seen in whatever short time I have spent in
data science is that in the initial phase
of my career I found myself in an environment
where I really did not have a mentor to go
to or a person who can help me grow in data
science.
So, what happens when you’re in such an
environment is that you tend to stay in your
comfort zone and you just keep on those things
which are very comfortable to you, what you
have seen.
But given how data science works or how vast
this field is, this is not a very good sort
of way to spend your time in data science.
So now what happens in such a scenario is
after 1 or 2 years of your work experience
when you look back and see what all have you
learned, there is not a lot on your plate.
That’s something that I would suggest people
avoid, especially early on, in the early stages
of their career.
Tell your story!
What I mean by that is, if you have a portfolio,
if you have had a bunch of things that you’ve
done especially in data science, the journey
matters a lot more than the outcome.
So, like I said, we need to see your approach,
so there was this problem, where did it come
from?
Why did you have to solve it?
What did you do in order to finally solve
it?
If you failed what could you’ve done differently
if you had the benefit of going back in time?
So it all, I guess it’s encapsulated into
that one statement when I say that “Tell
your story properly and faithfully”.
Write only things that matter, things that
you’re clear about.
Write only about your own contributions.
And be clear about it you know like be clear
about your expectations, and in general, make
it a short CV.
Don’t make it 30 pages long.
