There's some amazing videos out there about learning data science by Ken Jee, Tina and
Krish. Today i wanted to add my own flavour to the topic. Let's talk about how to learn data science
in 2021. I'll be giving you a complete road map to learning data science in just three months
with four to five hours of effort every day. Three months is just an average, it can take you
less time or more time depending on how quickly you progress with the course. I've intentionally
made this course very application and project driven so that you can keep yourself motivated
while doing the course. I'm Samanvay, a data scientist living in Sydney, Australia. Back when
I started in data science around five years ago, data science was not as popular as it is today. I
was lucky enough that I learned data science on the job which is a lot more application driven
and practical than learning data science through university or through an online course.
So when I sat down to create a roadmap for you guys. I made sure to make it as practical and
project based as possible because I truly want the learning to be driven by your own curiosity.
Enough talking for now, let's get to the roadmap. The roadmap consists of six two-week blocks, like
you can see here. By the end of the three months these are skills you would have learned and these
skills are essential in becoming an effective data scientist. And as we go through the detailed
breakdown I'll explain to you how it applies. Right off the bat, you'll notice that each of
the block consists of learning a course along with doing a project and you'll do both of those things
at the same time. The course is there to teach you the basics of the given topic for that block
whereas the real learning will be happening when you'll apply those skills into your projects.
Don't worry if you get a little bit lost with the projects to begin with you'll definitely figure it
out and if you have any problems Google is always there. Even in the workplace as a data scientist
we always google solutions to the problems we have. It's more than likely that 99%
of your problems has been encountered by someone else online so you're effectively standing on the
shoulders of giants to speed up your learning but don't just copy paste whatever anyone says
online. Try to work out why that given approach is the solution to the problem you're having.
As you start with the projects you'll learn new concepts and face new problems. For example if
you decide to analyze a data set and you're not really sure how to open up a csv file and convert
it to a data frame then once you've googled that you'll figure out that pd.read_csv is the answer.
So it's going to be quite an iterative process. You'll be faced with a problem, you'll try to
figure out the answer and you'll learn along the way. So this is very similar to the top down
approach that Krish mentions in his videos. Now let's dig a little bit deeper into each of those
blocks and I'll recommend you courses and projects to do. All the links will be in the description so
don't worry about writing them down. The first two blocks will be about learning to code in Python.
Python is going to be your bread and butter in data science. The learning resource for this will
be learnpython.org. So by the end of block 2, you should have completed the learn the basics and the
data science tutorials as part of that course. In addition to this, you'll also learn how to install
Jupyter notebook on your computer. This is going to be the tool you use for completing all the
projects I talk about today. Additionally in block 2 I also want you to look at this short tutorial
about object oriented programming. This is a type of programming that is one of the best approaches
to writing clean production ready code. The project for block 1 will be doing some basic data
analysis on the house prices data set on Kaggle. You could do things like finding unique values
in different columns or finding a mean of the sale prices. Take it easy with the project and let your
curiosity drive your data analysis and I know it might seem a little bit scary to use pandas just
in your first two weeks but you'll get through it. The project for block 2 will be doing some
data visualisation with the same house prices data set. For this, you'll be using Matplotlib. Here's
a blog post that'll teach you all the basics for using the package. Producing effective plots is
essential for being an effective data scientist because a lot of the times you'll be required to
analyse different data sites and produce insights and present them in a visual format because your
stakeholders don't want to be inundated with the details and the nuances of the data sets
and they definitely don't want to look at a pandas data frame. So learning how to create
beautiful and meaningful visualiSations is always a great skill to have. As a rule of thumb I would
recommend spending half of the four to five hours a day on the course and the other half
on the project. Let's move on to block 3. In this block you'll be learning the basics of statistics.
For the course component the learning resource will be the statistics and probabilities course on
Khan Academy. Pay extra attention to data distributions, hypothesis testing and study
design because often as a data scientist you'll be required to test data products through the
means of A/B testing and then you'll have to use different hypothesis testing methods to
quantify your results and figure out whether or not the results you've gotten are statistically
significant. The project for block 3 will be to create statistical plots of the house prices
data set. You can do things like plotting box plots or making correlation plots of different
features. This will be very similar to the data visualisation tasks that you did at the end of
block 2 but in this case you're more interested in creating visualisation that gives you an insight
into the data. You could say that you're applying more of a data science-y approach rather than just
creating pretty graphs. If you're enjoying the video so far then consider subscribing. I make
weekly videos about demystifying AI. Now let's talk about blocks 4, 5 and 6. These blocks will
focus on learning data science principles for blocks 4 and 5. You'll do the Machine Learning
course by Stanford on Coursera and by the end of block 5, you should have completed all the
way up to the end of week 3 of that course. This will teach you the basics of a linear algebra,
linear regression and logistic regression you'll also have to use Octave very briefly
but it's very similar to python so you definitely won't have too much trouble picking it up after
blocks 1 and 2. In block 6, you'll do an intro to machine learning on Kaggle this is a pretty fun
course. It'll teach you the basics of decision trees, random forest and about machine learning
pipelining. Now let's talk about the projects that you'll do for these blocks. For block 4,
you'll work on doing an exploratory data analysis on the titanic data set on Kaggle. This is a great
opportunity to apply all of the skills you learned in blocks 1, 2 and 3. For block 5,
your project will be building a simple logistic regression classifier using Scikit Learn this is
the data set. You'll be using on Kaggle and if you need some help with this task then you can
check out this blog post but obviously don't just copy and paste as is. Try to understand
how each of those steps is necessary. Finally for block six, you'll be working on the famous
titanic survival challenge on Kaggle for this try to use either a decision tree or a random
forest so that you can apply your learnings from the Kaggle course straight into this challenge.
And if you get stuck you can always check out the highest rated notebooks on that project from this
roadmap. I've left out SQL, model deployments and more advanced AI topics like neural networks, deep
learning and NLP because these are very involved topics. That will need another roadmap on top of
this one. This roadmap is simply a crash course into data science if you're starting from zero. If
you need any help from me or have any questions, then leave a comment below or you can shoot me
a dm on instagram. If you enjoyed the video then smash the like button and i'll see you next time.