Are you doing software testing for past
many years and now want to transition
into data science?
You are not alone! I get this question so
many of the times and in this video I am
going to provide you
some useful and effective tips that can
help you make a transition from testing
to data science.
First of all this transition is
certainly possible. There are many who
have made this transition and I'm going
to share
one story of Bindiya Rajendran
who
described the story on Analytics Vidhya
platform and the link is in video
description below. So you can read
through it.
But she made this transition after
eight years of working as a software
test engineer.
So let's uh go through
some of the tips that can help you. So
the tip number one
is
first you need to figure out in which
role you want to make the transition
because when you talk about data science
as a field
there are various roles- data analyst,
data engineer,
data scientist.
Data analyst among all these is the most
easiest one relatively.
Relatively data analyst is
easier than data engineer or
data scientist.
So maybe you can start with data analyst
if you are very good in math and
statistics you can
start with data science
data scientist as your target
or if you are very good in software
engineering you can
make a data engineer your ultimate
career objective.
But all of these roles have some common
things into it and I have a video which
describes the difference between these
three
roles, so you can watch it you will get a
full understanding.
So the tip number one- I would like to
share is
in your current job
you are working as a software test
engineer you don't want to leave your
job all of a sudden
so to prepare for data science migration
you have to start using
python to automate your day-to-day
boring tasks.
As a
test engineer you might be doing manual
testing or automated testing using tools
such as selenium.
In day to day life you have so many
manual tasks you know you might be
getting some extra file doing some
analysis or you might be performing same
repetitive tasks. These are very easy to
automate in python. Python is a very very
easy language to learn even a high
school student can learn it easily.
I have a python programming tutorial
playlist. If you watch just first 10 or
15 videos
you should be fine. You can easily
start working on it.
So learning python to automate the task
is tip number one.
As a test engineer you are working on
some three tier software application
which will have some type of database.
In that database
you can now perform some basic data
analysis you can use python and pandas
for doing that analysis or you can use
any BI tools such as power BI or tableau
So let's say I have an insurance data
set
or a
sales data set
you can perform basic analytics
such as what was my sales,
how was my sales growth in last three
months, how does the sales look like
based on different geographic regions.
If you don't know power BI
I have done a very good project on power
BI like a real life power BI project
and that
playlist is quite popular. I have
received comments from some of my
subscribers saying that after doing that
project they actually got a job as a
data analyst.
So follow that project and try to apply
on data analytics you know try to build
dashboard. Like dashboarding is a very
basic thing that you can do.
All you need is some kind of database
Wherever there is data- there is data
science. So you can there is always a
scope of building dashboards.
If you are working on a
database
as a test engineer. Now I know it's not
your part of your regular job so you
have to spend extra effort on weekend,
during the night
to build
these analytics dashboards and then
present it to relevant people in your
company.
These people could be your business
manager
or
your company will have a data
science department.
Try to build relationship with the
people in data science department. There
will be data analysts, data scientists.
You talk to them
you say okay I have built this dashboard
do you think it's useful
or
you can say
I want to learn data science, do you have
any tasks where I can help? I will. I want
to help you so that I can learn and
usually people are nice some people
might say no but you will find one or
two people who who would be willing to
help. So the key here is persistence if
you get no
don't
get demotivated. Keep on trying
and
on job learning
is often the best because
you are building something
tangible
that can be
useful in the company
and
the
best advantage you can get out of it is
you know inter company department
transfer.
I have seen enough examples where within
a company the person is working in a
different role as a test engineer
and
they can transition to a data science
department within the same company that
transition is easier because you know
interviews won't be that tough.
It won't be as tough as as if you are
applying in a
outside company. So
migrating to data science department
within the same company is the best
thing that can happen.
Many times as a test engineer you are
working on logs.
So you can do some log analytics using
python, pandas or some of the BI tools
and you know build nice dashboards and
present the findings to engineering team
or your business manager, or your test
engineering manager.
Now
when you decide to transition sometimes
people have this dilemma that hey I have
worked as a test engineer for five years
now if I'm migrated
am I going to start from ground zero
and the answer is no.
There are two important skills that you
have built as a test engineer that will
be extremely useful in your data science
journey. Skill number one is your critical
thinking ability as a test engineer you
are finding bugs in your software you're
thinking about diverse workflows in your
software and trying to find bugs you
know by doing critical thinking.
So
if you realize or not
over this period of time you have built
solid critical thinking abilities that
are very very important when you start
working in data science industry. Another
skills you have developed is domain
knowledge. Let's say I worked as a test
engineer in healthcare industry
or retail industry
I have gained
lot of domain knowledge, you know, I know
a lot of processes
and the domain knowledge is extremely
important when you go to data science
industry. It's not all about learning
machine learning or python pandas. Those
are two skills but the core skills
which is communication domain knowledge
business understanding core skills are
extremely
important and you have those core skills
so when you transition you're not
starting from ground zero you're already
halfway the other half way you need to
cover
by learning
specific
tools. Now I'm going to
share
certain
common tips which are
applicable.
Doesn't matter
if you are
transitioning from test engineer or
mechanical engineer etc.
While you're doing your nine to six job
thinking about transitioning to data
science
the first thing you should do is
start learning
data science skills in parallel
and this you can do by watching Youtube
videos such as my channel has a lot of
Youtube a lot of videos on data science
a tool skill set
you can also enroll into courses you
know there are so many e-learning
platforms. You can buy a course for like
10
and you can learn those skills there are
many boot camps. Boot camps are basically
two to three month
fast-paced learning programs
that you can join and learn those skills.
So learn those skills
in your extra time
on your weekend.
If you want to go to Youtube
search for
code basics data analyst
three months roadmap. I have a complete
roadmap where you can learn data analyst
skills in three months and if you want
to learn data science skills you can
search for code basics data scientist
six month roadmap. So I have a six month
roadmap for data scientist and these
roadmaps covers all the free resources
You don't have to spend any money you
can just learn things on your own all
you need is
good internet connection a willpower and
a laptop.
So learning skills on your own while
you're doing job
is a very important tip. The next step is
after you have learned these skills
you want to build
a project portfolio.
The project portfolio which you can put
on your resume which you can highlight
on your github page.
So how do I do
data science related projects?
Well my channel has a lot of projects so
in if you go to my channel and search
for
codebasics and data analyst projects I
have couple of projects and the popular
project is sales insight power BI
project. So in power BI me and my friend
we build a analytics dashboard
using the sales data
and by following this project so many of
my subscribers have got a job I'm not
kidding. I'm getting so many messages on
Linkedin saying that this project helped
them get a job.
So
my youtube channel is one way if you are
looking for machine learning projects, I
have another playlist for machine
learning projects where I did a project
on Bangalore property price prediction
image classification.
Even I built a mobile app for potato
disease classification using deep
learning and these are end-to-end
projects they cover model building,
making application, writing server in
fast API. These are like end to end
project how you would execute these
projects in
corporate if you got a job full-time job
as a data scientist.
So by following these projects you're
learning important skills and what you
need to do is
customize these projects so that you can
make it a unique project for yourself.
It's not like you're copying my project
for example for image classification- I
did
spot celebrity image classification now
you can do the image classification for
movie personalities or some spiritual
personalities or even your family
members. You can customize it there is so
much creativity. So you use my projects
as a reference but then customize it for
a different data set
and make it your own
unique project.
You can also
go to nearby NGO for example and ask
them
that you can help them for free.
Nowadays non-profits will have
a three-tier application they will have
databases and wherever there is data
there is data science. There is a scope
of data science you might have to try
multiple times
and you might get a success. If you're
offering your help for free to NGO or
even to your relative who might have a
small business who might have some
database, ask them to build some
dashboard for free like BI dashboard is
an is a no brainer. You can do it anytime
you have a database you can
build cool BI dashboards in power BI and
tableau very easily.
By working on
real projects you are learning a lot of
real life skills it's not like dummy
titanic data set project. You know
then comes kegel participation on
kegel.com you can go and
participate into competitions.
I did a video with Tanul Singh
who is a mechanical engineer.
And after his study in a mechanical
engineer every day he would spend few
hours on kegel and he built a good
reputation on kegel and after his
finishing his degree in mechanical he
immediately got a job as a data
scientist. So your kegel rank performance
are going to be super helpful.
Now if you
feel alone in your journey you can make
partners Codebasics
has a discord server
where
you can go to partner and group finder
channel
and find buddies. You know you can find
friends online friends and you can do
group studies. Group study has many
benefits you keep yourself motivated,
accountable, you learn from each other
and you generally networking is always
useful. So
use discord server- the link is in video
description below.
Let's say now I have learned the skills
I have a good project portfolio, I have
built a resume. For resume also I have
different videos so you have a
build a solid
one page resume. A resume should not be
more than one page. Doesn't matter even
if you have 30 year experience
one page resume
now you have one page resume what do you
do well
you start applying for internship.
There's a portal called intersharla. I'm
not marketing for them but there are
similar portals you can even use uh your
Linkedin network
You don't build a name a network on
Linkedin with data guys
data guys and girls.
Now these data folks are basically data
analyst, data scientists working for
different companies
and
you can send them a connection request
saying that I want to learn data science
I want to connect with you.
You can have one of phone call
conversation offer your help to them and
so on and by building these great
connections.
What you're getting out of it is
whenever their company have any
requirement they will be happy to refer
you
or when you're following the Linkedin
post you might get to know about
next data science job opportunity. So
this way Linkedin networking is very very
very important. Okay. So you build your
Linkedin network. You went through
portals like intersharla and you managed
to get internship
now the confusion arises- I am doing my
usual
50 000 rupees a month job
you know whatever the amount my maybe
and now I want to
take this internship which is paying me
you know only 20 of my current salary
should I go for it? Well
it depends on your
on your intention. Like how serious you
are about data science transition if
you're serious about it
take few months
salary cut
do the internship once you have
internship in your resume
finding job will become more and more
easier. So you do one or two internship I
know for maybe one year you will be
earning less but it's worth
because once you get a job in data
science industry, these jobs are very
high paid and you can make up for your
loss you know during the internship time
period.
The last
tip I want to give is
many times folks will
go for data science master either in
your own country or abroad.
If you go for data science master you
are now spending dedicatedly
two years in your master's you are
learning all the skills the degrees on
your resume and after your master of
course finding job is going to become
easier because then if someone looks at
your resumes okay data science degree
they'll be like okay the person has at
least spent
two years of
dedicated time
in studying data science. So
that is my last tip. But I know
you'll have to spend money, you'll have
to take a break from your job
so
be
thoughtful if you're going that route
but
the other tips that I discussed below
are actually enough to make a transition.
Now
one thing you have to remember is when
you're making a transition of course
it's gonna take time.
You will keep on applying in different
companies you will not get a response
Don't get
demotivated.
Use that time you know maybe start doing
open source contribution.
Start helping people on discord. Start
participating on kegel.
So while you're getting
rejection from all your jobs, you're
still
making your data science skills more and
more stronger and eventually you're
going to get an opportunity. I wish you
All the best of luck. If you have any
question post in a comment box below.