How to Learn Data Science in 2021?(The Complete Guide)

How to Learn Data Science in 2021?(The Complete Guide)

If you are looking to become a full-time data scientist and AI expert from scratch then you are in the right place.

Today I am about to reveal some easy ways to start learning data science.

Follow these steps and learn data science easily.

Before I dive into that I want to make one thing clear it’s true that the job of a data scientist seems attractive and lucrative from the outside but learning data science has its difficulties and challenges.

Now do not let that bother you.

Tips to Learn Data Science

Tips to Learn Data Science
Tips to Learn Data Science

I will help you tackle these challenges by sharing with you five tips on how to learn data science easily.

1- Learn Data with Deep Interest

Learn Data with Deep Interest
Learn Data with Deep Interest

This is the most important tip so I decided to put this one right at the beginning.

Few people ever talk about motivation and learning.

It’s difficult to find the motivation to learn a new subject.

It’s even more difficult to stay motivated when you have to learn a subject that you are not passionate about and remember self-learning is not only difficult but also quite lonely.

Now let’s consider data science.

Data science is a vast and fuzzy field.

This makes it very hard to learn.

If you are not truly motivated you will stop even before you reach the halfway mark.

You might even believe that you cannot do it and believe me it is not your fault.

The fault lies in the teaching.

The secret to continue learning even when things seem difficult is your ability to stay motivated.

Imagine it’s past midnight and the formulas start looking blurry and you begin to wonder if recurrent neural networks will ever make any sense.

It’s at times like this that you need tons of motivation to move forward.

You need something concrete that will help you find meaningful linkages between linear algebra, statistics and recurrent neural networks.

This something should prevent you from the inevitable struggle that every new learner faces.

What do I learn next?

It’s precisely at this point that you need motivation and I am not talking about an inspiring quote here.

I am talking about a passion project that will drive your learning and help you progress.

Imagine you are working on the Walmart sales forecasting data set.

You are probably aware that the retail industry is at the forefront of exploiting data science for driving business revenue.

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Areas such as inventory management customization of offers and product placement are sought to be improved all the time.

Walmart is one such retail brand that applies data science to achieve improvements in these three areas.

This data set provides detailed information on the historical sales figures of the various departments of 45 stores of Walmart.

Your goal is to forecast the department-wise sales figures for each store.

You can do this using the historical data over 143 weeks.

Let’s say this project is your entry point to data science.

Some of the first programs you code to predict the sales figures across the various departments in each store may not involve any statistics but the first time you begin coding your programs might not perform well.

You might need to work day and night to improve them.

You might need to be obsessed with improving the performance of your programs.

This is what I meant when I said you need to learn to love data first and actually this is your motivation and as you work you will start to learn to love data.

I understand that everyone may not be obsessed with forecasting the sales figures of Walmart stores but it’s important that you find one thing that pushes you to want to learn.

It could be finding the real positions football players play.

  • Figuring out interesting places to visit in your city,
  • Mapping civil war refugees in Africa by year
  • Tracking house price movement over the decades in different cities

The interesting thing about data science is there are countless captivating things to work on.

You need to develop the ability to ask the right questions and find a way to get answers.

It’s important that you take full control of your learning by personalizing it to what you really want to do.

It simply cannot be the other way around that is you do not want to end up learning something that does not interest you.

2- Learn Data Science By Doing

Learn Data Science By Doing
Learn Data Science By Doing

Once you have a clear picture of what you want to learn the next logical step would be to learn it by doing it but before I cover learning by doing in detail let’s take a quick look at the wrong way of learning data science.

A typical new learner will prepare a long list of books to read and courses to take.

This approach to learning data science is totally wrong.


Many new learners who take up our courses on Udemy tell us how demotivating it has been to be simply given a big list of resources that had no context.

You will struggle immensely with this approach.

Learning data science this way will prove to be an uphill task and if you opt for this method of learning data science you are only setting yourself up for failure.

What is the easiest way for you to learn data science?

What topics you should learn?

You learn data science best by building and trying out things yourself.

Academic studies have shown that beginners learn best by doing.

In data science this implies that you learn best by working on projects.

When you work on projects you gain real world skills that you can apply immediately.

The problem with data science textbooks is they focus on theoretical concepts too much.

When you do not apply what you learn you tend to forget it easily.

Beginners make the mistake of focusing too hard on theoretical concepts while they undergo a training course.

As a result they fail to give equal importance to the practical applications of the theoretical concepts they are learning.

Remember! you can only learn data science by actively applying what you learn.

You also need to understand that your studies are aimed to prepare you to do data science work in the real world workplace.

  • Increase the Level of Difficulty Gradually
  • Once you complete a couple of beginner level data science courses you need to seriously consider taking up advanced courses.

You cannot afford to have the mindset of a beginner all the time.

You need to become proficient in complex data science concepts.

The best thing you can do right now is to take up more difficult courses.

Increasing the degree of difficulty along your learning pathway constantly is the right approach.

For instance if you are reasonably comfortable working on a particular project you can consider learning a new data science concept.

It’s time you moved on to more difficult concepts.

Data science is decidedly a steep mountain.

Sometimes it seems so easy to stop climbing but the problem is if you slow down your learning journey will become even more challenging.

Hence whenever you find you are getting too comfortable follow the ideas outlined here.

They will add more complexity and challenge to the data science projects you undertake.

This approach will push you out of your comfort zone and it would be even better if you could try two or more of these ideas at the same time.

  • Work with a bigger data set
  • Make your algorithm faster.
  • Explore different options to scale your algorithm to several processors.
  • Understand the theory of the algorithms you are using better.
  • Interact with your online classmates and discuss with them the theoretical concepts behind the project you are currently working on.
  • The last activity is a pretty useful one give it a try.

You will certainly see that it makes learning data science a pleasant experience.

You will likely gain a deeper understanding of the subject you discussed with your classmate than you had before.

You will also improve your communication skills in the process.

3- Join an Online Learning Community

Join an Online Learning Community
Join an Online Learning Community

The main advantage of joining an online learning community is you can make a meaningful contribution from the comfort of your own home and you do not need to worry about your lack of public speaking skills.

As you get to know learners who are at the same level as yourself you will begin to enjoy a sense of community and this is what can keep you going even when the learning journey seems difficult.

It’s also good to know that you can talk to people who understand everything you are going through and it’s easy to talk to people who can relate better to the learning issues you are facing.

4- Learn from your peers

Learn from your peers
Learn from your peers

You can talk to your community about your studies and you can easily exchange ideas activities and notes.

You can organize weekly meetings and discuss the project you are working on or simply talk about how your learning progress during the week.

These meetings will keep you inspired and help you to stay on course in your learning journey.

When you work with others it’s really amazing how much you can learn?

Teamwork is an essential part of learning data science.

Even in a job setting there’s so much teamwork involved.

You will quickly find that collaboration is extremely important in this field.

Some ideas to learn from your peers include:

  • Contribute to open source packages
  • Find people you can work with at meetups
  • Message people who write engaging data analysis blogs
  • Ask them if you can collaborate
  • Check out data science competition sites and find a teammate

As a beginner you can exploit the online community even more to learn.

You can find useful information about example data sets exercises and pre-packaged solutions.

The ongoing interactions with your peers will certainly make learning data science a pleasant experience for you.

Another proven approach to improve your practical data science skills is to take part in data science competitions.

Do not let the fact that you are still a beginner discourage you.

A data science competition will help you augment your skills even if you are a beginner.

5- Update Your Data Science Knowledge Constantly

Update Your Data Science Knowledge Constantly
Update Your Data Science Knowledge Constantly

Data science without a doubt is one of the fastest evolving fields today.

The only way you can keep up is by learning constantly.

You will quickly find that what was relevant just a few years ago is no longer relevant.

This is the reason you need to keep updating your knowledge all the time.

Data science blogs present you with the most cost effective way to learn more about this field.

You can gain an inside perspective on everything related to data science.

You also get an opportunity to stay informed of the most recent happenings within the industry.

Data science blogs offer you useful advice on learning resources as well as opinions from authority figures and current news and trends.

Best Way to Update Your Data Science Knowledge

As already mentioned in the previous point joining a peer group is the most cost effective way to update your data science knowledge.

A peer group will keep you motivated. Motivation or rather staying constantly motivated to learn more is a decisive factor when you are keen on learning data science.

At times it can be a bit daunting when you have to do it all alone but an online peer group helps you stay in touch with a bunch of folks who share identical learning goals.

When you interact with people who are pursuing similar data science courses as yourself learning new concepts can seem less burdensome.

The other thing is you can have compelling technical discussions over the internet.Some of the online forums that present you with this kind of environment include:

  • Data quest
  • Analytics Vidya
  • Team for data analysis
  • Data scientists
  • Stack exchange

Final Words

By following all of these step you can learn data science from scratch to advance. Make a community of learning. For more help about data science you can contact us any time.


I am Blogger and a Website Developer. Student of Information Technology. Working as a Professional Blogger and Web Developer.

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