Are you a mechanical engineer electrical
engineer or even a physiotherapist?
Basically a person from
who thinks you're not from a data
science background and who wants to
build a career in data science. I'm going
to discuss three ways you can do this
the first way is you can find a job
in your own field which can help you
make a transition to data science career.
For example
if you're a mechanical engineer or
industrial engineer you studies the
principles of supply chain management
based on that knowledge you can get a
job as a supply chain
analyst you don't need to know Python
Tableau etc as a supply chain analyst
you need to know
basic Excel reporting and the principles
behind supply chain management which you
have studied as part of your industrial
mechanical engineering. Once you get
job as supply chain analyst start
studying Python, Tableau
all those tools and you can easily get a
job as a data analyst.
I interviewed one of my subscribers Ayan
Chakravarthy.
He was a mechanical engineer, industrial
engineer.
he got a job as a supply chain analyst
then he he transitioned to BA analyst.
BI analyst. Eventually he's a data analyst
right now here in U.S. He recently got
four job offers. I'm gonna do interview
with him if
in interview that interview is available
I will link that interview so that you
can
know his own journey. So that's one way
you can also by the way get a job as a
business analyst.
I interviewed Apoorva who is a data
scientist at Amazon. She joined Amazon as
a business analyst. Now,
as a business analyst you don't need to
do no Python math
etc you need to know domain you need to
be
good in communication presentation etc
you know
generally street smart business manager.
And once you're a business manager you
know
domain knowledge maybe your work as with
data analysts or software engineers
then you can acquire technical
skills which are required to transition
into that role
and within a company it's sometimes easy
to transition. So
in amazon switch from business analyst
role
to data scientist role. There are enough
stories I'm going to link all those
video interviews in a video description
below so you can if you are interested
in knowing the story in detail. You can
watch it.
So yes you can join a non-technical role
such as business analyst and then
transition to data analyst.
If you are a student you can work on
projects which requires
use of data science. I interviewed
Rishikesh Bosale he is a mechanical
engineer
during his engineering he participated
in a formula race challenge. I mean he
wasn't a driver
driving the car but he was doing a
mechanical engineering
project around formula race and he used
the principles of thermodynamics for
that project.
But there was a lot of data in that
project so he started exploring
mathematics and these tools such as
tableau, you know,
which will be helpful in terms of data
visualization.
So here is a mechanical engineering
students he's doing his regular
mechanical engineering project
but he is using data science tools in
that.
So if you are in college try to pick up
a project where there is a scope of
using these tools and
almost whenever you have data there is
always a scope of using this data
science tools.
I interviewed Padru Pradhun as well who
is a petroleum engineer.
He also did his college project in his
own field
which is petroleum engineering. But it
required some kind of predictive
modeling,
some kind of mathematics. So he used all
of that he learned machine learning
from code basics channel only and then
he got a job at Halliburton which is a
big
petroleum company. But his role in that
company is data scientist.
So your domain knowledge as a petroleum
engineer electrical engineer
healthcare is not going away. That's a
very useful
thing that you need to have as a data
scientist or data analyst.
So you just need to accompany that with
some technical skills you know so that
was number one
which is finding a job or a project in
your own field.
Second one is freelancing and
participating in competitions.
I interviewed Kasim Bhatti he started
freelancing on Upwork actually
he did not have any experience he did
not work in any company after his
graduation.
He started learning these tools
online like the sales insight project
that he studied from my channel.
He made some modifications and he got
very similar project on Upwork
so for that he has to make some only
minor modification and he on like 50
which was a small amount for the first
project but then he built a reputation
and in four months he became a top-rated
Upward freelancer. He's earning more
money
than what he can earn in his job he's
doing data science data analyst as a
freelancer sitting at his home.
So freelancing is an awesome way if you
are working let's say somewhere
in your job try to find freelancing
opportunities. What I mean by that is
let's say you're a mechanical engineer
you are doing some type of
you're dealing with some type of data in
Excel. Well you can write a Python script
maybe you can do some Pandas EDA there
is always a scope
friends. Be creative if you be creative
there is always a scope
data is everywhere and wherever there is
data there is data science
in your job as mechanical engineer,
electrical engineer, whatever it is
or try to find a creative way of solving
problems using data science tools.
If your management is not supportive
enough let's say
and if the situation is said that you
cannot do such projects at your job
maybe you can do some freelancing
outside,
you know, reach out to some local NGOs
and provide your help for free reach out
to your relatives who might have a small
business who might be doctor,
you know, lawyer, everyone uses software
and when the wherever there is software
there is data
and wherever there is data there is data
science. So you can
reach out to all these folks provide
your help for free.
You know no one will say no most likely
and you will get to learn a lot and you
will be working on
a real project you are not working on
some dummy titanic dataset project.
So freelancing is number two
way of transitioning from different
field
to a data science field. The third way
which is very obvious
I don't need to probably say this but
it's data science master
right you there are many colleges
universities who are providing data
science
bachelors and masters. I have a friend
who lives here in my town he's
also my subscriber he is a
physiotherapist
and he is doing bachelors in
data science so you can do data science
bachelors, masters
doesn't matter what your field is. You
could be from a healthcare domain, any
domain.
Your domain knowledge is going to be
very helpful so when my physiotherapist
friend i really wish he gets he studied.
He completes his study soon and he gets
a job as a data scientist
when he will get a job probably he is
going to get a job in health care domain
because he has worked all these years in
health care domain.
And that domain knowledge is going to be
super duper useful.
I hope you like these three ways of
transitioning
to a data science career if you have any
other
comments or any thoughts on what I just
said in this video.
Please post as a comment in a video
in a video comment box below. I'll try to
answer the questions, you know. I try to
answer most of the questions which are
posted in first two or three days. Thank
you for watching.