hi friends welcome back another video in today's video i'm going to share how i would learn data
science if i were to start all over again a lot of you have been requesting this video so i thought
i would share my thoughts and things that I've learned that i now if i get to redo what i
would do differently if you have why some of my previous videos you know that i am a self-taught
data scientist i actually did not go to school to study data science my background is in business
and i had to make that transition through self teaching through online courses
but there were definitely mistakes that i made and now looking back let's say if i were to do
it all over again this is how i would do it and if you find with information in today's video helpful
give it a thumbs up and subscribe to my channel if you want to hear more about data science tech lab
cell and everything else in between so the first thing i would do which i think i made a mistake on
is i just jumped in i heard the word data science the sexiest job and i just jumped in like
data science is a huge job family there are dozens of jobs within the data science and honestly i had
no idea about this and to me the jaw family that was thrown at me or jaw family that i
was introduced to was a data scientist generalist role so when i jumped into learning data science
that was the role i targeted but eventually i realized that there's actually a lot more
options that i could have considered if i were to start all over again for you who are just trying
to figure out who want to get into data science and you're just trying to figure out your path
what i would want you to do is study the data science field start understanding
what are the different job families within it what it takes what is the type of work that
you would need to do to basically whatever the type of work that you will be doing as
in that role and then align it to your interest and your skill set figure out where you're going
to be good at and figure out where you're going to enjoy working and then follow that path so that's
the number one thing that i would say is research spend a lot of time studying online reading
talking to people who already work in that role to get a better idea so that's number one thing that
i would do instead of jumping into one role that i heard that i have heard of i would actually take a
step back and try to see like within data science what are my options and what do i need to do
to go from point a to point b but you would need to do a lot of research to figure out that point b
before you start creating a plan the second step i would suggest is let's say you have picked data
scientist generalist as your role that you want to target so the second step that i would suggest
we're not done researching yet we're going to do a lot more research so look at the data scientist
generalist role and read the job descriptions at different companies so study the role at different
companies and let's say if you have an ideal company study the role study the job description
for that role and try to understand what are the requirements that you would need to kind of check
to be able to qualify for that role and also a lot of people don't do this go on linkedin if you have
already that role that description go on linkedin and study the profiles of people who are already
working in that role because that's going to give you a really good idea what are the things that
that person did that you need to do to basically be able to be qualified per se so yeah that's like
my second thing that i wish i had done i actually did it later on but that was i think that was too
late so if i were to start all over again i will study the data science job family i would also
once i have narrowed down the role that i want to focus on i would study the role and read the
job description at different companies the third thing i would do is there's a lot of advice that
is out there that tells you to start with coding i would not do that i would actually let's say if i
pick data scientist journalists as my role i would not pick coding as the first thing i need to learn
i would pick the theoretical concepts when it comes to statistics when it comes to machine
learning when it comes to business understanding and i would have a very solid understanding of all
those things before i actually get to the coding part i personally i think learning to code is
great but that's just remember that coding is just a way is a tool to apply data science it's not the
data science itself there's a lot of people who know python but they are not data scientists so
think of language the picking the coding language as a tool to apply data science but you need to
understand data science conceptually understand the fundamentals before you get to the next stop
because if you jump into coding the first thing you are actually skipping a step and
you're jumping into the coding uh piece of it where i would start is statistics like get really
really good at statistics because no matter which role you are in you are statistics is part of
basically all the roles within data science so you cannot go wrong learning statistics and if
you need more detail on this topic like i did one of the videos where i talked about why learning
statistics is mo very important as the first step to learning data science i would recommend you to
watch that video i have done a detailed video on this if you haven't watched it i suggest you to go
watch that video where i talk about why statistics learning statistics is important for data science
let's say you have done learning the theoreticals you understand data science conceptually very very
well now is the time to apply the concept that you learn on data that you're working with so
here i would suggest to start learning to code so see the steps that we did so step number one
was researching the job family because data science is a huge umbrella second researching
the role once you have narrowed it down third getting really good at the concepts and um
understanding the fundamentals and theoretics and then the fourth step is when you actually
start doing the coding start learning the coding and for coding languages i don't think
there is any wrong choices but for me i from my personal experience i actually started with sql
and r and um looking back i wish i had started with sql and python i also did a video on this on
why which language i would choose python versus r i would definitely choose python and you can watch
this video where i go into a lot more detail and give real-world examples of why i pick python over
r but um i would say like if you are trying to decide like python is a good choice so
pick sql python sql is a must no matter which role you are in the data science i think a lot
of the people have to make a decision do you want to learn python or do you want to learn r if you
have the option to choose i would suggest picking sql and python now that being said sql nr is also
pretty good so i don't think any of these choices are wrong it all comes down to personal preference
and remember if you have done the research in step two where you understood the role
and you understood the job requirement that will actually help you decide what language you need
to pick and what area you need to focus on that's why researching is very very important piece of
creating your plan to learn data science all right so now let's say you have
done your groundwork you have done the learning fundamentals as well as coding
now it's time to do the projects so now it's time to take everything you learn
and turn it into a project and kaggle is actually a great place to start where you can
look at other people's projects and you can look at what other people have done and there's a lot
of data available on kaggle so that's a great place to start to kind of figure out how you
can build your project portfolio and then build your project portfolio i'm planning to do another
detailed video in terms of like how to build your portfolio but just know that if you have done the
research on step two you actually kind of know what kind of project you need to do to be able
to stand out for that role i'm recommending that you get an idea what type of projects will help
you qualify for that role or for those roles that you are targeting so that's where you need to do
another connection between the project work that you do and the project the the job requirement
and people in the projects that other people have worked on who currently work in those roles so
do a lot of projects build your data science project portfolio where you are able to show
what you know and you're able to apply the concepts that you learn the theoreticals
as well as you're able to do the hands-on work to show that you actually know what you're doing and
remember when you are trying to work on those projects remember that start with the problem
and end with the impact and whatever you do in the middle make sure to kind of highlight that as well
but i see a lot of people miss the problem and the impact part and they only focus on
let's say if you work build a decision tree they will be talking about like i build a decision tree
to do this but why did you do that what was the impact of it so make sure like to follow that
framework start with the problem and end with the impact and in the middle you can talk about like
how you solve that problem to get to the impact part so those were the five first five things
that i would do if i were to learn data science again so let's say you have done that there is
one last step that i think a lot of people miss because a lot of people start thinking that
you have done the work and i made the same mistake that you done the study you did the projects and
you built a portfolio now you should get the job that's actually not true you need to now prepare
for the interview i'm sure you have heard that interviews is a completely different monster in
itself where it doesn't really reflect like i'm going to say this it doesn't really reflect how
well you know it actually reflects how well you know in a limited time frame under in a pressure
setting so let me say that again the interview process is trying to see how much you know in a
limited time frame where you're given a problem and you're able to solve in a limited time frame
under in a pressure setting that's completely different thing from the day-to-day work to the
interview and i know that's like a completely different topic this is a different topic for
another video but just know that just doing your data projects doing the learning is not enough to
actually land the job now you have to put in the work to start interview preparing do the hands-on
coding prep where you are solving problems in a limited time frame and do a lot of
mock interviews brush up on your fundamentals the things that you learn brush up on that do a lot of
mock interviews with your friends and try to see where you are good at and where you can improve
and then iterate on that all right so those were the six steps that i would do if i were to start
learning data science again so let me know in comments what has been your process for those of
you who did do the self teaching who did follow the self teaching route what was your process
do any of these steps surprise you or resonate with you let me know in the comments and with
that thank you so much for watching i hope you're having a beautiful day i'll talk to you later bye