My Data Science Learning Track

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This post contains online & university courses, as well as various content, reference materials, subjects which I have covered throughout my data science journey for the past 2 years both studying my Master’s Degree. I have provided links and references where possible but some courses I took from the university don’t have an online reference, thus I have summarized my learning from them briefly.

General Data Science Learning

Microsoft Professional Degree in Data Science

MS Degree in Data Science is an online degree program, developed by Microsoft and This degree was the first major introduction for me into the world of Data Science and this is how I got excited about the area. The degree covers major topics and subjects necessary to do data science projects. It consists of varios units and you can take either R or Python track or both. For more information, check out the program here.

Microsoft Professional Program in Data Science on

Dataquest is a great platform not just for beginners but also for those who have already had some data science education or learning. This is primarily due to the fact that the platform is very hands on and enables you to work with real-life datasets and learning by doing. One of the benefits of the platform is also the community. As a premium subscriber you get access to Slack and get to meet many interesting people also working towards their data science career. For users with premium subscription, dataquest offers calls with in-house Data Scientists, which is a great opportunity to ask questions. I have joined dataquest in 2018 and still enjoying it.



MIT Open Courseware (OCW)

Math is super important for understanding various machine learning algorithms and numerous topics in Statistics. When it comes to learning or refreshing your math background I think MIT OCW is a great resource. I personally have taken the following three courses from MIT OCW. Please note that I did not have college-level math degree as part of my Bachelor’s.

Single Variable Calculus

Single variable calculus by Prof. David Jerison is a great start since it covers topics such as differentiation, integration and other topics which are fundamental to learning data science. The course is structured very well and I would strongly advice to take it for those wishing to either refresh their knowledge as well as those starting to learn.

MIT OCW Single Variable Calculus

Multivariable Calculus

Multivariable calculus is an extension to single variable calculus in that the topics covered are similar but apply to cases of multivariate analysis. Prof. Denis Aroux is very good at explaining various topics. This was the second course I took after single variable.

MIT OCW Multivariable Calculus

Linear Algebra

Prof. Gilbert Strang is quite popular in the domain of linear algebra. His course on MIT OCW is one of the most popular and his books are used across various institutions to teach linear algebra. The course gives you a good background into understanding matrix algebra, various operations and concepts behind it. I strongly advice taking the course and following it up.

MIT OCW Linear Algebra


3Blue1Brown is a youtube channel that offeres great video courses/explanations into the intution behind certain concepts in calculus and linear algebra. They are short and concise, but nevertheless they offer nice insights into the concepts. I quite enjoyed them and here are the three playlists I watched through.

3Blue1Brown - Essence of Calculus
3Blue1Brown - Essence of Linear Algebra
3Blue1Brown Channel


As part of my Master’s Degree I have also taken Higher Mathematics, Mathematical Statistics & other courses in order to have necessary background for Math and Stats.

Machine Learning & Statistics

UT Machine Learning

University of Tartu (UT) is one of the leaders in computer science education in Europe and I would say the world. UT is ranked in the top 1.2 % of the best universities in the world with a very strong computer science, math & statistics departments. During my studies at UT I have followed a Machine Learning course, where we covered major topics and algorithms used in Data Science, starting from linear classification/regression to deep neural networks. As part of the courses we followed Peter Flach’s “Machine learning: The Art and Science of Algorithms that Make Sense of Data” book. The course was really hands and we implemented different algorithms in python from scratch. At the end of the course professor Meelis Kull has organized a private kaggle competition, where my team (‘Friends’) got in the top 10 (10th spot) out of almost 66 teams and individuals.

The course is publicly available since UT is a public university. By the time of the writing of the post the course is available via the link below.

UT Machine Learning
UT Kaggle Competition


Introduction to Statistical Learning (ISLR) is an excellent resource to learn machine learning from more statistical perspective and dive into the inner workings on various machine learning algorithms. The book provides an excellent overview of various machine learning methods with practical examples and applications in R.


UT Business Data Analytics

Introduction to Probability & Statistics

MIT 18-05 is a great introductory course on Probability & Statistics. It covers major topics such as probability distributions, central limit theorem, bayesian inference and regression.

MIT OCW Introduction to Probability & Statistics

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