The
prerequisites required in order to learn data science now. Many of the
subscribers have been asking this particular question even I'm getting messages
on LinkedIn saying that I'm from commerce background can I move it to data
science domain, what are the prerequisite crèche, I'm a mechanical engineering
student can I move into data science and many more kind of questions are
actually coming up.
Always remember
one thing guys data science is a technique which can be applied in any domain
if you have a specific use case where machine learning or deep learning fits
definitely go ahead and you can apply in that particular domain. So I'm going
to mention some of the prerequisites that you need to have so that you
can easily make a transition towards data science. I'm going to mention four prerequisites:
i)
the first prerequisite is that you need to have
some basic programming knowledge now when I say to have some basic programming
knowledge like C and C++. If you have done some programming initially, making a transition towards data science can be very very easy because of some of the
programming languages that you will be used in data science such as Python is a
very easy programming language to learn,
ii)
the second prerequisite is that you need to be
at least good at maths, you should also have some interest in maths if you
don't have any interest and if you're not good at maths guys, I think it'll be
very very difficult because if I talk about data science if I talk about every
modules in a lifecycle of a data science project like feature engineering,
feature selection, model creation everywhere maths is actually involved.
iii)
The third prerequisite that I'm actually
specifying is which is called statistics, again guys math and statistics
both are important. Now if I just talk about statistics I am basically talking
about data and are taking up the data from any use case you are doing a lot of
analysis here. You're trying to work on the population. You are trying to work
on the sample of data. In short, it is all interrelated with statistics so all
the mathematical equations that you will be applying will be basically applying
on that specific data. Both Math and Statistics are very much important, this
is a very important prerequisite, and I know most of the colleges that you study
about statistics is all about Mean, Median, and Mode. It is not just that guys
if you just try to consider a very good use case you'll be able to learn a
whole lot of things and again if you are not good at math side I would suggest
that you go and follow a Khan Academy of statistics they're a whole lot of videos
different use cases that I actually discussed over there and it is explained
pretty much better
iv)
the next prerequisite is that you need to have
some basic knowledge of databases. Database plays a very important role and in a
data science project because all the data initially stored in some databases
and then you pick up that particular data, then you do all the feature
engineering process, feature selection process and you do basically you finally
create a model and do the deployment. Some basic knowledge of database is also
very important because at the end of the day, data will be stored somewhere
someplace and you have to retrieve that particular data and do all this
particular process. It is very important that you need to have the knowledge of
databases like SQL MongoDB, No SQL, SQL database and some other type of types
of databases. So if you have that particular knowledge and if you are also a
big data engineer who has actually worked in Hadoop database architecture and
all definitely if you move towards data science you will basically be called as
a full-stack data scientist because you know the big data part also you know
the data science part also that basically means you will be able to do all the
related work with respect to the data science.
So, yes these are the pretty much important prerequisites that basically
required, and this prerequisite, if you have definitely your transition towards data science, will be smoother. If you don't have then you have to put some more
effort while you're learning those things.
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