and don't know where to start well this video is perfect for you because I am going to talk
about how I would learn data science in 2023 if I were to start all over again I did a version
of this video in 2022 and I like to refresh it every year with my updated thoughts based
on the industry Trends in the data science job family so in this video I'm going to summarize
six steps that I would take to learn data science we would be talking about what research you need
to do and then we'll jump into what skill set you need to learn followed by what do you need
to do after you're done with all of that as we go through the steps I suggest you take notes
I'm going to link down a notion template below which you can use when researching different
data science roles different data science job family and for creating your study plan number
one thing that I would do to become a data scientist in 2023 is I would research the
role when I started to learn data science I heard that data science is the sexiest job
Emily thanks to the Harvard Business review and I jumped in I wish I wish I had done the research
try to understand the data science job family a little bit more to understand what encompasses
the job family and what are the different options in there before I jump into one in 2023 there are
many roles within the data scientist jaw family and I'm going to list a few of them there is data
Engineers machine learning engineer data scientist product data scientist data analyst just to name a
few so if you are just starting out and don't know anything about these job families I would suggest
that you read up on each of these role and try to understand what they do before jumping into any of
the roles that are in the data science job family because this step will give you actually a really
really good idea what the expectations of the role are and what you will enjoy doing so let's
say you have decided that you want to become a data scientist generalist you can think of
it as like full stack engineer who does back-end friend that the middle and everything in between
you can think of it as like a full stack data scientist that does pretty much everything they
know statistics machine learning they know coding they can take the business problem and apply data
science to solve those problems and add value to the business so let's say you figured out that
data scientist role is what you want to do you're not done researching yet what I would suggest you
to do is go read the job descriptions for the data scientists role at different companies
that you are interested in as I mentioned in some of my previous videos data scientist is
a slightly difficult for Deaf Family to be in because the definition for a data scientist is
not well defined from company to company so this is why you need to put a lot of attention on this
step because your study plan your roadmap is going to look different depending on what companies and
what specific roles that you're trying to Target let's say Amazon is the company that you want to
Target and you want to become a data scientist there you will go to the job description for the
data scientist open roles at Amazon and you will try to understand what are the skill sets required
second you will go to LinkedIn look at people who currently work as a data scientist or have worked
as a data scientist at Amazon and try to look at those people's educational background what
did they study then the type of projects that they've worked as a data scientists basically
we're actually doing this research and we're basically actually doing data science here
statistics we basically ran collecting samples of people and trying to understand what are the what
are their educational history what is the type of project that they do this is also a good time
to look at if they're like specific certificates that they have these people have specific degree
programs specific boot camps that these people took that basically helped them so this will get
you actually a lot of information so let's say you've done your research where you're
ready for the third step the third thing I would do is I would learn the fundamentals
of data science you probably were not expecting to hear that because a lot of other advice that
you have heard is basically is telling you to start coding I strongly believe that in order
to be a data scientist a good data scientist you actually need to have a solid Theory knowledge of
statistics and machine learning before you get to the coding part and there's a reason why the
coding languages is a way to apply data science they are not the data science itself you can ask
any data scientist who is currently working in the industry or is going to school you can ask them
like what is data science they will tell you data science is pretty much statistics domain knowledge
and machine learning knowledge so that's why it's important for you to build those fundamentals
before you start getting into coding now here one thing I would say that if you hate coding then
I would not pursue a data science because data science does require coding and if you have never
coded in your life in that case I would suggest you to try out coding first before you jump into
the theory but theory is so so important and it basically builds the foundation for data science
so I would suggest you to learn statistics and learn the machine learning fundamentals before
you jump into coding and we're doing this step but we're not going to go too deep we're going
to go at the high level you're going to try to understand what statistics actually is you're
going to try to understand what machine learning actually is so before you jump into the next part
let's say if somebody comes and asks what is the difference between linear and logistic regression
you should be able to explain it that's the level of knowledge that I suggest you to have
before you jump into the coding part the coding part would actually when you apply the knowledge
you will actually get to learn statistics and machine learning much more but for the initial
learning period I would suggest to stick to the fundamentals learn statistics mathematics and
machine learning so these are the three areas that I would suggest to build your fundamentals
on so here is how I would approach it for math I would be very comfortable with linear algebra for
statistics I would get pretty comfortable with probability distributions hypothesis
testing Bayesian versus frequencies so those are basically the basics in statistics the third I
would suggest you to go into machine learning and machine learning try to understand what are
the types of machine learning there is supervised unsupervised reinforcement and then within each
try to understand what is a regression what is a logistic regression what is classification
what is decision tree we're not going too deep but we're understanding it enough that we we
can explain theoretically what they are because we're gonna go back to it again now a word from
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out it might be a good fit for you now back to the video so let's say you build your Theory you have
a solid foundation in statistics machine learning now it's time for you to go to the next step which
is learn to code there are many languages that you can learn for data science for the simplest
it is take I would suggest to start with SQL and python I personally started with sqlnr r
has a very steep learning curve and it's not as intuitive as python but R has a lot of ready to go
statistics methods available to you that you can use for for doing like a very quick analysis but
art cannot be productionized by whereas python can live in a production environment I would
suggest you to start with python and SQL python is super intuitive and as an industry I have seen
that python has actually been taking off because it's easy to understand by everybody involved in
the project including software engineers and data scientists for learning SQL and python
SQL you can you can go with any Learning Resource for learning SQL it's pretty straightforward you
just need to understand how to join different data sets in our right left self joins which is like a
weak point for me how to do SUB queries how to do window function for python now there are a lot of
courses that are out there for a learning python here I would like you to pay special attention
to focus on learning python that is specific to data analysis for example there are several
libraries that are focused on data science and data analysis and machine learning such as pandas
numpy scikit-learn matplotlib just be mindful when you're picking your Learning Resource for python
make sure that it has a focus of data science because there's a ton of learning material out
there that teaches to software engineers and non-software Engineers I would suggest you to
focus on python that is more targeted toward data science at this point you have a really
good understanding of fundamentals of data science which is statistic machine learning math you have
coding knowledge which is SQL and python now is actually it's time to apply the skills that
you have learned so far and turn it into a project there is a possibility that while you are learning
all those things learning by python learning SQL through whatever Learning Resource that you use
you probably already did the projects so you have like some hands-on experience but this step is
specifically to build your portfolio and to get you more Hands-On knowledge on how to do things
start building your project portfolio and remember the first step that we did or the second step that
we did where we were looking at different people on LinkedIn who are working at your target company
in that role if you have already written down what type of projects that they do in their role
as a data scientist this will give you actually a very good idea of what type of projects to Target
what kind of focus areas that you need to have in your project portfolio if you're looking for
data there are actually a lot of free resources available to you where which has tons of data
and problem sets that you can use to build your product portfolio listing a few including kaggle
Google data search US Census Bureau and you don't have to be limited to the data that is available
in this platform you can actually make your own data for example you can look at your your
purchase history on your credit card download that data turn this into a data science problem
and do a project project on it identifying your purchase trends for example How likely are you
to buy a coffee if it's raining what I'm trying to say here is like you can look at different
data set make your own problem and try to build projects around it build at least five projects
get your data from kaggle Google data search or make up your own data Target a domain that
you are interested in but also a domain where you want to get into for example if you want to
be a data scientist in e-commerce then you would pick a data data set that is related to that and
solve a problem that is related to e-commerce I would also suggest if you have the option is to
build the online project portfolio link it on your LinkedIn link it on your resume and build
a GitHub portfolio this is optional I personally didn't do a GitHub portfolio but if I were to do
it again and I don't have any experience I would build a GitHub portfolio so recruiters can look
at it and they have additional information on my skill set that they probably don't have on other
candidates who don't have GitHub portfolio the reason you're learning trying to become a data
scientist is to get a job if you are doing it just because you're curious like that's great but most
people who are trying to become a data scientist they want to get a job so the sixth step that I
would recommend is to prepare for interviews and the reason I say this is because a lot of people
discount how much work interviewing is knowing the skill versus doing it in an interview setting
where you are under a pressure environment and you have to answer in a time constraint manner
I would start practicing on a platform like lead code or start a scratch I'm going to link it below
and that is for SQL and Python and then for your fundamentals in your theoretical knowledge I would
start mock interviewing and start practicing with a friend have them ask you questions so
that way you are ready for the interview itself I've created a detailed video on how I prepare
for interviews and what is my process you can go and watch it here I'm going to link it somewhere
here it goes in a lot more detail that I'm going in this video so hopefully by the end of this
process you are able to go into an interview and perform to get a job offer now that being said I
do want to mention that over the last few years given that there's so much interest in the job
family it has become more and more competitive so don't be discouraged if you don't get your job on
the first try so this is the process that I would use if I were to learn data science all over again
do any of these steps resonated with you surprised you let me know in comments with that thank you
so much for watching this video and I will see you in a different one have a beautiful day bye