Welcome to 5 Mins 5 Ideas. I'm your host Samanvay. Today we're going to
talk about 5 things I wish I knew before
becoming a data scientist. 3.5 years ago I took the
leap into data science from being an aerospace engineer and during that time
I learnt a couple of things that'll be extremely helpful to you
if you're thinking of diving into data science. The first thing i wish i knew
is that data science is not just training models.
Before I started working as a data scientist I thought that data science
was just training models. My naive notion of data science was that you would
collect data from somewhere not really sure how and
then train your model on that data and then you'd
go on to predict a random bunch of outputs and i wasn't
too sure what you would do with the outputs but i thought that the majority
of the time in that data science cycle was solely devoted to models
and I was really wrong as I got to find out
model is less than 10% of the work. You'll be working on
a lot more than just models and this can include data collection,
labeling, data verification, process management and monitoring. This
definitely does not sound as exciting as models
but that's the reality if you're trying to build big
scalable systems. Now there's definitely roles out there if you want to get more
experience with models and these are at consulting companies. At
those companies you'll be exposed to a diverse range of
industries and projects the projects can last from just
a couple of months to about a year. The downside is that
it's unlikely that your project will be deployed at a large scale but the
reality is that building fully fledged AI systems takes a long time and a
company might have to dedicate many years of technical effort to get to
a point where they have a fully functioning autonomous ml pipeline.
The second thing i wish i knew is how important coding is
in data science. Having a good coding foundation is important at being
successful in data science if you're already
interested in data science i'm sure you've checked out the online data
science courses most notably Machine Learning by
Stanford. The main aim of these online courses
is to give you a rundown of all of the ml algorithms
used in the industry and they might even give you a bunch of code snippets that
you can copy and paste to get an algorithm
up and running. Now all of that is fine if you're getting started with data
science but if you really want to level up
i'd definitely recommend being proficient in a coding language and a
good one to get started with would be python but even if you have
a java experience or some good experience in an object-oriented
programming language it's not going to be too hard for you to
pick up python i'd also recommend learning bash just so you know your way
around terminal as well as version control using git one
plus point of learning coding is that it allows you to build skills
that will be important regardless of whether or not you choose
to go into data science as those skills are
equally as important in other software engineering disciplines.
The third thing I wish I knew is that data science job ads
can be very misleading data science is a very new field. Given the high amount of
publicity around this domain a lot of companies
want to implement AI in their workplace but aren't too sure
exactly what skills they need or whether they've even got enough data to do
anything with so what they end up doing is just googling what kind of skills a
data scientist often need and they just put all those skills in
that job ad. So you can often find data science job
ads that want the candidate to have skills in
SQL, Python, Scala, Hive, Hadoop, spark and that's definitely an
alarm bell. Because that just means that they've
exhaustively listed all the technologies that a data
scientist might use and they're not too sure about the
direction of data science in their own company so I would focus more on
job ads that list a couple of technical skills but
also focus on soft skills that you might need such as communication skills or
presentation skills and I'd also recommend looking at the overall
reputation of the employer who has posted that job ad as well as the
culture and both of which you can find out more
about on glassdoor reviews, which i found very useful on my
hunt for data science jobs and also once you get past that resume
review stage definitely make sure to grill the
technology teams at the companies you're interviewing so you can gauge
exactly how serious they are regarding data science and machine learning
because this helps you make sure that you end up at a company where you're
going to be surrounded by lots of smart people that you can learn
more from. The fourth thing I wish I knew before becoming a data scientist
is how important communication skills as well as attention to detail
are. Communication is important in any field including
technical roles because it's important in making sure that you can work well
with your team and you can communicate problems and
propose solutions accordingly but it's especially
important in the field of data science because data science teams
tend to sit between engineering teams and the sales team
so they need to be well versed in converting technical details into big
picture summary because as a technical person
it's very easy to get bogged down in the details you have to
remember that you're working on a product
that you're gonna be serving to a client who might not be as technical as you are
and sometimes in other technical professions you can get away with not
interacting with the client so much but this is
definitely not the case in data science. The second thing I talked about
is attention to detail this is important for a couple of reasons
one of them is that good attention to detail can help you
figure out the shortcomings of your training data which helps you down
the line in making your model perform better and it'll also help you highlight
the edge cases that your model fails for so it can't be understated
how important attention to detail is in data science especially if you're at the
cutting edge and you're trying to squeeze out every single percent to
level up your model accuracy. The fifth thing I wish I
knew are the different types of data science jobs
out there. So I've got three categories for those the first
is product enhancement with data science. So that's improving an existing product
in the company that you're working at with
ML techniques so it could be ML working behind the scenes of the product
in a non-obvious way so it could be something like google search
or it could be for detection on credit cards using anomaly detection.
The second type of data science jobs is ml as a product so that could be
ml which serves as the sole product for a particular application.
So a good example of this is autonomous driving. This is
primarily done by image segmentation on the fly
as the car is driving. So the sole product there is machine learning as
autonomous driving would not be possible without it. The third type of data science
job is in decision making now that could be a role
of an insights analyst so this is a type of data scientist that'll be working
inside of a company to figure out how best to
optimize the different processes in order to cut cost and increase
revenue. An example of this could be analyzing the product usage of your
clients to figure out what the best way is to lay the features
out to improve usage. Another example could be to use machine
learning to figure out if any cost in the finance department is
blowing out of proportion compared to five years ago and that
could be a simple form of forecasting. That wraps up five things
I wish I knew before becoming a data scientist.
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