The Ultimate Learning Path to Become a Data Scientist and Master Machine Learning in 2024

Pranav Dar 28 Feb, 2024 • 4 min read

The Learning Path to Become a Data Scientist in 2020 is now live! Head over here to start your data science journey.


Learning paths are immensely popular among our readers and with good reason! Learning paths take away the pain and confusion from the learning process. For those who don’t know what a learning path is – we take the pain of going through all the resources available on data science, machine learning and Artificial Intelligence, select the best ones, and arrange them in a logical sequence for you to follow.

If this sounds like a lot of work – it is. And it is done with the purpose of eliminating that huge amount of work you would otherwise have to do. We understand that when you are starting out in data science, the sheer amount of resources can be overwhelming. And hence we created learning paths. And our learning paths were an instant success with our community.

If learning paths were useful in 2018, this year they would be the most helpful tool for a person trying to become a data scientist. Why? Because the amount of content and information out there has increased multi-folds. And so has the confusion and the amount of knowledge expected out of a data scientist.


Access the complete learning path to become a data scientist in 2019 here. You will need to register yourself on our training portal – this will enable you to keep track of what you have covered.


Summary – Learning Path to become a Data Scientist in 2019

Broadly, the learning path to become a data scientist can be divided into the following steps:

  1. Getting Started: The biggest step of them all – beginning your data science journey. This stage is all about understanding what data science is and what a data scientist role entails. Additionally, this is where you should pick up the programming language and tool of your choice (our recommendation is Python). This will enable you to code through all that you learn in the coming months
  2. Learning Basic Maths and Statistics: What are the core concepts a data scientist must absolutely know? That would be statistics and mathematics. Where learning a tool will help you perform quick calculations and generate results, you can’t truly become a data scientist until you have a solid grasp on statistical methods (probability, descriptive and inferential stats) and mathematical fields (linear algebra to be precise). That’s why we emphasize on these two fields in this year’s learning path
  3. Learning Machine Learning concepts and applying them: This is where things start to hear up – you’re reading this article because you’ve been intrigued by what machine learning can do. And once you’re done with the above points (by March start if you follow the learning path), you will start learning the basics of machine learning. But this isn’t just limited to theoretical concepts. We firmly believe in learning by doing, hence we have provided some awesome projects so you can experience what a data scientist does!
  4. Some more applications of Machine Learning: Once you have a good grasp on these basic techniques, we move along in April to more advanced topics, like ensemble learning, random forest, boosting algorithms, and time series methods. But ML isn’t limited to just the algorithms, you need to know nifty tricks to improve your model, right? That’s where validation strategies and feature engineering will play a role. We also encourage you to keep your focus on industry applications, and have hence included a recommendation engine project in the learning path
  5. Introduction to Deep Learning: Now you know these machine learning concepts, what comes next? Deep learning of course! It’s becoming an essential part of any data scientist’s CV these days. July should see your data scientist path lean towards understanding neural networks and getting the hang of Keras
  6. Various deep learning architectures like RNN, CNN: Follow that up with a deep dive into advanced neural network frameworks, namely recurrent neural networks and convolutional neural networks. These are fairly heavy concepts, hence we recommend spending a few weeks on understanding them from scratch
  7. Computer Vision Applications: Computer vision is one of the hottest fields right now and hence we have focused a lot on this domain. We feel every data scientist should absolutely have this on their resume since this is where a lot of the jobs in the future will come up. We have included a really cool project to give you a practical understanding of how a computer vision model works
  8. Natural Language Processing (NLP): No data scientist learning path is fully complete without first going over NLP. You should focus on learning the basics at the very least, including text preprocessing and text classification. If you’re feeling adventurous, you can explore how deep learning works in NLP but that’s not a mandatory requirement

We have broken down all these steps month on month – so if you start following the learning path, you know exactly what you need to follow and what you need to cover every month starting today.

You can access the full learning path here and register yourself to start your journey today. Our training portal enables you to track your progress after each section thus helping you to stay on track throughout the year.

Here is an image laying out what should you do month on month to become a data scientist by the end of 2019. If you put in all the efforts as mentioned in the learning path – you will be well placed to get into a data scientist role before the end of the year.

We have one more gift to make your new year truly special. Join Analytics Vidhya’s CEO and Founder Kunal Jain on January 10th for an exclusive webinar where he will elaborate on how to get the most out of this learning path. He will discuss the roadmap to become a data scientist in 2019. Get your questions answered and your doubts clarified by one of the eminent personalities in this field!

We have done all the hard work to make sure you have all you need on 1st January to become a data scientist this year. Now, it is your turn. As always, if you have any questions – do let us know. See you more often on Analytics Vidhya in 2019!

Pranav Dar 28 Feb 2024

Senior Editor at Analytics Vidhya. Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

Frequently Asked Questions

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Responses From Readers


YASH PAL 01 Jan, 2019

Thanks for giving this information. Am looking for it..

Peter 02 Jan, 2019

Anything for Apple store?

Hitman2552 03 Jan, 2019

What is the difference between this learning path and the learning path for Deep Learning, Are both learning paths applicable to freshers???

JOSE VILLEGAS 04 Jan, 2019

Thanks for this material, I really want to complete the path.

Kanav Sharma
Kanav Sharma 04 Jan, 2019

I am beginner what to choose path for machine learning or deep learning . As your path for deep learning also include Machine learning i am bit confuse

Hitman2552 04 Jan, 2019

If we complete this path, will we be industry ready and will companies like to hire us as we are freshers???

Sharanya 07 Jan, 2019

I want learn machine learning

Amit Tewari
Amit Tewari 08 Jan, 2019

Would be great if the resources for covering the specific topics were also mentioned along with the plan.

Deepak 10 Jan, 2019

Hey Pranav, Thanks for sharing this invaluable information, but there's a small suggestion. I'm an avid follower of AV and would love to see the courses linked to each section on the learning path just like the older versions of 2017 & 2018 where we have a MOOC course link for every step in the path. I understand AV has spent much time in curating the information and have created a free course for this learning path with all the materials we need at one place, but it's always good to have a second source of information just in case someone isn't able to understand something from one source. Just a thought!

Sohail 16 Jan, 2019

Hi , I aim to become a data scientist .in oder to fullfil my ambition which path i need to choose .am confused what to choose either Ai,Ml,DL .kindly suggest me so that I can move forward . thanks in advance

Kbaah 17 Jan, 2019

How do you transition the progress made in 2018 to 2019 LMS?

Sasank Majumdar
Sasank Majumdar 19 Jan, 2019

Will I be industry ready after I finish this path? Will any organization be interested in hiring me? Does this learning path any industry recognition?

pallavi 27 Jan, 2019

is it a free course??

Shruti Arora
Shruti Arora 01 Feb, 2019

thank you for uploading this post. You clear every doubt about machine learning and deep learning. please update similar type of post on machine learning

Jitmanyu 02 Feb, 2019

Hi. I am 46. I have academics in computer engineering.I am AMIE. But I have never practised IT. I possess 15 years experience in BPO, quality, process excellence, compliance kyc and statutory audit. Can I get in data science field and more important can I get a job and successfully change my career? Thanks.

Anand 18 Mar, 2019

I have close to 9 years experience in IT industry - mainly with midrange programming and SQL. Now, I would like to switch to Data Science roles. Is that advisable? If yes, what should be the path that I choose?

Dr S Kotrappa
Dr S Kotrappa 03 Apr, 2019

Nice very useful and innovative of learning Data Science, thanks

Dr S Kotrappa
Dr S Kotrappa 03 Apr, 2019

Nice very useful and innovative way of learning technique of Data Science, thanks

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