This post is to provide you with an end-to-end Data Science learning path — a journey that begins with Linear Algebra, going over to classical Artificial Intelligence all the way to advanced Machine Learning topics like Reinforcement Learning and Deploying Machine Learning models. This will be updated regularly.

Let me start by stating the reason why having such a learning path is necessary and why it will prove to be extremely valuable to you. As a Data Science professional, I get these questions on a daily basis:

“How do I start learning Data Science?”
“Where do I get the resources from?”
“Which course will teach me everything?”
“Do I need to have a certain degree to work in this field?”
“Do I have to be extremely good at Math?”
“Do I have to learn programming?”

I am sure if you plan to begin your data science journey, or even if you already have, there are points where you need some sort of structure or a direction in terms what needs to be done next. And if you are anything like me, before doing anything you would always look at its prerequisites, and then if those prerequisites have their own prerequisites and so on… you get the idea…

A Learning Path is the chosen route, taken by a learner through a range of (commonly) e-learning activities, which allows them to build knowledge progressively.

Wikipedia

And in my opinion, this is exactly what learners need now. Not everyone has the time or money to attend college or universities and some really good content is available online.

But this abundance of choice has created another issue. People usually feel most comfortable when they understand all the available options and can easily compare and evaluate each one. But I don’t believe things work quite like that when it comes to education. Picking up an online course even for a thing such as how to play a guitar is a hassle. Believe me, I have faced this myself!

And how did I handle this situation? By doing absolutely nothing about it. I have seen many people around me and online who do the same thing — The planning itself on how to begin with anything takes so much time and effort that the actual thing gets delayed again and again. That is where these learning paths come in handy — the plan has already been created for you — and this one has been created specifically for a career in data science.

This learning path will guide you and help you in staying focused and on the right track — while also showing you how intricately all the fields and subjects are connected to each other. Just remember, you don’t have to go full hardcore and do everything from start to finish. I have highlighted the most important concepts/topics under each subject — you just have to make sure you go through all the material around these. But at the same time, also know that anything you do apart from these will only prove to be beneficial to you, so there’s absolutely no harm in learning a little extra!


Learning Path — Topics

Following are the topics which make up the learning path. The topics have been put together and placed in a particular order which to me makes sense. We start with Algebra going all the way to learning about how text is encoded all this while working on interesting datasets from super helpful data repositories like Kaggle.

  • Algebra and Matrices
  • Statistics — I
  • Analytical Thinking
  • Calculus
  • Programming — Concepts and Implementation
  • Data Structures and Algorithms
  • Database Management
  • Python / R for Machine Learning
  • Exploratory Data Analysis
  • Data Visualization
  • Statistics — II
  • Artificial Intelligence
  • Machine Learning — I
  • Natural Language Processing
  • Deep Learning
  • Machine Learning — II
  • Machine Learning Problems
  • Software and Platforms

The learning path which has been created at thedatascienceportal for 2020 will be updated regularly to keep the reference material fresh and up-to-date. Get the complete learning path in PDF wherein each topic’s most crucial concepts are mentioned along with the reference material. Download it now!

Data Science Learning Path 2021

In this article, I wanted to share a learning path for Data Science which covers all the important topics and concepts. Let me know in the comments if you feel like I missed out some important concept or maybe a topic entirely. I hope this learning path guides you well and saves you a lot of your time and energy. I hope you liked this article, let me know what you think about the content in the comments!
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Thank you for reading!