today's video is sponsored by shortform more about them later in the video. hi friends,
welcome back to another video in today's video we're going to talk about things i wish i knew
before i entered data science that i wish i had known before to set my expectations
because i didn't get a reality reality check after getting into the field so hopefully if you are new
to the industry or you are planning to get into the industry after watching this video you'll have
a better understanding of things about the data science style family that are not often discussed
so it'll set your expectation so you won't get any surprises when you are in the job family
when you are working in the industry for me the number one thing i think that was the biggest
surprise that was for me was that that data science job family is huge there are so many roles
within the data science java family that i had no idea existed so when initially when i started
in the domain i was introduced primarily to data scientist role and typically the data scientist
generally has role so when i was preparing for to enter the field i was primarily focusing on that
role because that was that's all i knew but after entering the field i actually realized there are
so many jaw families within data science and the reason the data scientist title probably get the
most uh coverage is because harvard's article that they wrote a few years ago calling data scientists
the sexiest job of 21st century but they forgot to mention that there are actually so many job
families that live under the data science umbrella so there's obviously data scientist generalists
that everybody talks about media gives a lot of coverage and all of that it is data analyst
which is also pretty popular thanks to youtube and thanks to a lot of data analysts in the industry
there is a so there is data scientist generalists there is data analyst there is machine learning
engineer then there is applied scientists there is research scientists there are statisticians there
is ai scientists and then i'm just listing a few of many roles that are currently available in the
industry under the data science umbrella so when i was like preparing myself so i'm a self-taught
data center so when i was preparing myself i was preparing myself for the data scientist generalist
role because that's all i knew so for those of you who want to get into data science industry i want
you to do some research and figure out which job family do you want to enter and like study all of
them and see figure out where you see yourself fitting the best and then work toward that so
don't go narrow minded into one job family because that's what's been getting most of the coverage
from the media so that was number one thing that i learned the second thing that i learned
was that there is an unglamorous side to the data science field and uh a lot of the time it's i
think it is starting to get a lot of coverage but it's still not talked about often when um somebody
is selling you the job family we all know there's a lot of data and there is a lot of dirty data
so a lot of the work is actually somewhat of repetitive grunt work to clean that data
before we can actually do anything useful get any insight or any sort of insight information from it
there's a lot of data available to us even a lot of the work i would say like 50 fit more than 50
of the work is actually doing the data cleaning and let me tell you it's not as glamorous as
that some people have portrayed the data size gel family to be because you do spend a lot
of your time figuring out what is wrong with the data where the what are the anomalies how
can you normalize the data how can you make it useful to be able to use in a model or grab any
sort of information information or insight out of it so there is a lot of grunt work involved
cleaning the data and that that would depend on like from company to company some companies do
have a very solid data engineering teams and they do all of the create the data pipeline and have a
clean data available to you but there are a lot of companies that are still out there where the
data scientist is actually tasked to do the data cleaning before they can actually use the model
so just set your expectation because it's possible that you might be doing a lot of data cleaning
in your data science role before we move on to point number three i wanted to take a quick pause
and thank today's sponsor short form and tell you a little bit more about what short form is
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from in the description to check it out and now let's go back to the video the third thing that i
was surprised and that really caught me off guard was the job search yes i have talked about it in
one of my videos you can watch it job search was hard job search was very very hard for those of
you who are in the industry you probably know that the job titles and the job descriptions do
not really match from company to company and that makes your life very difficult when i did my last
job search i was working as a data scientist but i had to do a lot of groundwork to figure out the
data scientist definition for my role matches to the data scientist definition to another company's
role and for that i had to do a lot of reading on the job description a lot of talking to the people
who work in that company to try to understand what actually a data scientist at company x means so
that made my job search pretty hard additionally you can watch this video for more details because
of that the interview process was not standardized and it led to a lot of surprises during the
interview because i would be interviewing in company a which would auto ask me completely
set of different questions and company b would have completely different set of questions um
i'm saying like not different set of questions in terms of like different areas like one would
be like more let's say data analysis heavy the other would be more ml heavy so it was like all
over the place and i would go into the interview not knowing what to expect today because that's
how messed up this whole the whole data science jaw family is currently from the standardization
standpoint the fourth thing that i wish i knew before i got into the data science job family
was that data science is new and a lot of companies are just starting to realize the
potential of data science and people who work in the data science domain so that also means that
there are a lot of people when you are working in a new industry there's going to be a lot of
people who are not going to understand what you actually do um and that would mean that sometimes
you would get put on a get put on a project that are possibly that you are possibly under
qualified for or overqualified for so that is that means like you have to do a lot of due diligence
on your own to kind of figure out if this project is actual problem that you you should be solving
or should it be something somebody else who should be working on these problems um a lot of the
people like a lot of the non-tech and non-data science people would sometimes confuse you
for somebody who's just pulling the data for them and i have had i have i have received emails where
and those are like the worst kind of emails where i would get emails from somebody saying person
jenny let's say jenny is sending me an email and she basically has a long email thread going on
and she's forwarding me that email where she has a data request from somebody hired up and she's
asking can you pull this data for me and reply to me and then her expectation is that my job is
to pull the data for her and then send it reply directly to her and then she will take that data
and forward it for and do whatever she wants to do and like give it to higher ups on her own so
you will be dealing with a lot of those people so it's a lot of it comes down to
first you being part of a company and a team that actually understands what data science is
how to evangelize data scientists how to enjoy data scientists where to evangelize machine
learning scientists and if you're not in that one of those companies then that means you would have
to do a lot of education for people that you work with to make sure that they understand
what skill sets match what work projects match your skill set so something to keep in mind
as you enter the industry and my four my last i don't know if i'm on the fourth or the fifth one
my fourth i think i'm on the fifth one yeah i won the fifth one so my last learning that i think is
very very very important is doing the coding work doing the hands-on work writing papers building
models cleaning data it's great it's but one thing that i realized that what differentiates
a good data scientist a good data analyst good machine learning engineer good applied scientist
from a great data scientist applied scientist machine learning scientist is communication
communication is very very very important when it comes to any data science java family as
i said in my previous point that you will be working with a lot of tech and non-tech people
people who understand data science and people who don't understand data science you'll be working
with a lot of business stakeholders so this means that you have to be very good at explaining
your work in simple english terms and tie your work to business objectives so you can actually
get a buy-in on your project and you are able to communicate what your work does so if you
are entering into data science field make sure work on your communication communication is very
very important remember what differentiates a good data scientist from a great data scientist
is communication so start working on it if that's an area of your weakness and if
this is one of your strengths you're gonna be fine so those were some of my learnings
and let me know in comments what learnings you have from your experience working in the data
science domain so with that thank you so much for watching and i will see you in the next video bye