Favorite MOOCs for Data Scientists Favorite MOOCs for Data Scientists
I had asked on LinkedIn recently about everyone’s favorite MOOCs in data science. This post started a lot of great discussion... Favorite MOOCs for Data Scientists

I had asked on LinkedIn recently about everyone’s favorite MOOCs in data science. This post started a lot of great discussion around the courses (and course platforms) that people had experience with. Certain courses were mentioned multiple times and were obviously being recommended by the community.

Here was the post:

Biggest takeaway:

Anything by Kirill Eremenko or Andrew NG were highly regarded and mentioned frequently.

So I decided to revisit this post, and aggregate the information that was being shared so that people who are looking for great courses to build their data science toolkit can use this post as a starting point.

You’ll notice that below Coursera had the most mentions, this is mostly driven by Andrew Ng’s Machine learning course (11 mentions for that course alone) and Python For Everybody (6 mentions, also on Coursera). Similarly, Kirill has a number of courses on Udemy that all had multiple mentions, giving Udemy a high number of mentions in the comments as well. (Links to courses are lower in this article).

The 2 blanks were due to one specific course. “Statistical Learning in R” it is a Stanford course. Unfortunately I wasn’t able to find it online. Maybe someone can help out by posting where to find the course in the comments?

Update! Tridib Dutta and Sviatoslav Zimine reached out within minutes of this article going live to share the link for the Stanford Course.

There was also an Edx course that was recommended that is not currently available, “Learning From Data (Introductory Machine Learning)” so I won’t be linking to that one. If you’re familiar with MOOCs, a number of platforms allow you to audit the course (i.e. watch the videos and read the materials for free) so definitely check into that option if you’re not worried about getting graded on your quizzes.

To make the list, a course had to be recommended by at least 2 people (with the exception of courses covering SQL and foundational math for machine learning, since those didn’t have a lot of mentions, but the topics are pretty critical :).

I’ve organized links to the courses that were mentioned by topic. Descriptions of courses are included when they were conveniently located on the website.

Disclaimer: Some of these links are affiliate links, meaning that at no cost to you, I’ll receive a commission if you buy the course.


  1. “Sabermetrics 101: Introduction to Baseball Analytics — Edx” “An introduction to sabermetrics, baseball analytics, data science, the R Language, and SQL.”
  2. “Data Foundations” — Udacity“Start building your data skills today by learning to manipulate, analyze, and visualize data with Excel, SQL, and Tableau.”


“Mathematics for Machine Learning Specialization” — Coursera “Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning.”


“Tableau 10 A-Z: Hands-On Tableau Training for Data Science!” — Udemy (This is a Kirill Eremenko course)


  1. “R Programming” — Coursera “The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.”
  2. R Programming A-Z™: R For Data Science With Real Exercises!” — Udemy(This is a Kirill Eremenko course)”Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2″


  1. “Python for Everybody Specialization” — Coursera “will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.”
  2. “Learn Python” — CodeAcademy

Python for Data Science:

  1. “Applied Data Science With Python Specialization” — Coursera
  2. “Python for Data Science” — Edx “Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.”

Machine Learning:

  1. “Machine Learning” — Coursera (This is an Andrew Ng course)
  2. “Machine Learning A-Z™: Hands-On Python & R In Data Science” — Udemy(This is a Kirill Eremenko course)
  3. “Python for Data Science and Machine Learning Bootcamp”— Udemy “Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!”

Deep Learning:

“Deep Learning Specialization” — Coursera (This is an Andrew Ng course)” In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.”

No one had anything bad to say about any particular course, however, some people did have preferences in terms of platforms. You can read the original post yourself here.

I hope these courses help you widdle down the plethora of options (it’s overwhelming!) and I hope you learn some great new information that you can apply in your career. Happy learning!


Original Source

Kristen Kehrer

Kristen is currently a Senior Data Scientist for Constant Contact. After completing a MS in Statistics and a BS in Mathematics, she started her career utilizing Econometric Time Series analysis to forecast electric and gas load in the utility industry, leveraging Neural Nets and ARIMA models. Since then, Kristen has spent her career in Analytics and Data Science in both healthcare and Ecommerce. Her most recent position was managing a team at Vistaprint working to optimize the Vistaprint website and increase conversion. Kristen writes about different topics in data science at www.kristenkehrer.com.