hi friends welcome back to another youtube video in today's video i'm going to share some of my
learnings things that I wish i knew when i started my data science career journey for those of you
who know who are familiar with my career journey i'm actually a self-taught data scientist i did
not go to a school and study data science so a lot of things i had to learn while doing the job
and this is a video that i wish i had seen when i was starting out in data science so
i thought it'd be a good idea for for me to talk about this topic and share some of my learnings
so for those of you who are new to data science you don't make those mistakes before we jump into
the video make sure to give this video a thumbs up and subscribe to my channel if you want to hear
more about data science career tech lifestyle and everything else in between let's jump right into
it one of the biggest learnings for me has been is number one is don't try to learn everything
as i have talked about it in some of my other videos and i'm gonna link some of them somewhere
here data science is a huge domain so when you are just starting out it's very easy to get lost
let's say if you are like data analyst or data scientist whatever you're trying to do
like it's a very huge domain so it's very easy to get lost in it a perfectionist somebody like
me who would want to learn every single thing that is part of the data job family i learned it
the hard way that i actually didn't need to learn everything so my advice for anybody who's new is
try not to learn everything create a backward plan uh for example if you are a student and
you're trying to figure out what you should learn i would say like figure out what you want to do
if you have an ideal role an ideal company in mind study that create a plan work backward
and then focus on the things that you have figured out that are important for your ideal
ideal role so that helps you narrow down in terms of what you need to learn the second biggest
learning for me was uh and this is an interesting one i don't think we talk about it enough
understanding that not all of my work is going to get productionized let me say that again
understanding that not all of my work is going to get productionized this is something that i
learned in my in my last company where i would work on a lot of research projects assuming that
once i finish that project the business team is actually going to take that project and implement
it to my surprise that was actually not the case a lot of the times the project wouldn't get
productionized so let's say if i worked on like five projects only two or three of them would get
productionized and it is kind of like discouraging when you're produ when you work on something super
cool that you think is amazing it doesn't get implemented i want to say that it's actually okay
like a lot of the data science work is research focused so when you're talking about research
in research not everything makes it so just having that mindset just understanding that
it's great that you worked on something but possible that it might not get implemented one way
to avoid that is like you start working backward with a business problem with a business objective
and then work backward and then solve a problem so if you so that that's like one way to avoid this
problem i have realized like the projects where i worked with the problem first approach those do
end up getting more traction when it comes down to implementation and productionization of the
project so that was my number two tip number three i would say is and um this is not specific today
science i would say like this can get applied to any role is um advocate for yourself building
something doing something working on a data science project working on a super cool project
is great but you have to be able to advocate for yourself and for your work i've learned
that doing the work is actually half of the work advocating for your work is actually even more
important not just for like getting your project productionized but also like when it comes down to
performance reviews career development promotions and all those ratings and all those sort of things
advocating for your work and highlighting how exactly your work helped the business it's key
so for example let's take two people one person both of them worked on a really cool project
one person took that project like wrote a one paragraph on it highlighting how their project
has helped the business team and they share it now they are advocating their work and the people who
are who are reading those paragraphs if let's say that person is sending an email or whatever
all those people are now going to advocate for your work so have to do that
to be able to showcase your work so going back to the example person a also worked in a cool
project but they didn't do didn't advocate for their work they didn't quantify their impact
who's gonna have a better rating when it comes down to performance review gonna have a better
review so doing the work is important but also highlighting the work quantifying the impact
is even more important and my last tip and bonus tip which is basically for anybody
don't stay at a company or team or a specific product focus area for too long there are a
few reasons i'm saying that one is financially when you switch jobs when you switch companies
logically your compensation increases obviously like you get through those like performance
reviews and stuff and you get some bump but it's never going to be equivalent of what you would get
if you were to switch a company so financially it does make sense to switch around this is how much
the corporate america is designed the second is and i think that's even more important is
the learning aspect when you switch teams when you get introduced to new business domains new product
domains new focus areas new data sets you get to learn along that process in switching teams can
help you get to that if you are like working at a big company where you have the option to switch
team you can switch teams and get exposed to their business domain their product domain and different
type of data sets and different type of problems that you'll be solving and that's going to give
you the breadth which is very important especially if we're talking about a data science role or even
a data analyst role on the same note switching companies gives you exposure to different working
environment different cultures especially if you're new when i was at my previous company i
loved everything they did and to me that was my ideal because i hadn't seen anything better but
after leaving that company and working for another big tech company i'm realizing that there are a
lot of things that i see different and there are a lot of things that i actually enjoy in a company
culture that i didn't have at my last company so if you are new like i highly recommend to like
switch around and whether that you change a team whether that you change a company because it's
actually going to expose you to different work culture different learning style different data
sets different work environment and financially it makes sense to move around um i think we're
going to do a different video on this where we're going to talk about the constructor especially in
big tech where RSU is a big competent and when i do that video you'll actually it'll it'll all make
sense to you like why moving it out is actually more beneficial than sticking it on all right
that's all i wanted to say let me know if any of this resonate with you let me know in the comments
if there are additional learnings that you would like to share with everybody so thank you so much
for watching i hope you have a beautiful day and i'll see you in another video bye
you