Top MOOCs for Data Science in 2020 Top MOOCs for Data Science in 2020
Last year I wrote an article on this topic and I wanted to update it for the new year—2020. As I... Top MOOCs for Data Science in 2020

Last year I wrote an article on this topic and I wanted to update it for the new year—2020. As I teach introductory data science courses at UCLA, I embrace new learning resources with eyes wide open. I’m always looking for quality education options to recommend to my students after they run through my process. All the previous reported resources are still very good choices. In this article, I wanted to throw a number of additional MOOC (massive open online courses) resources on the table, and also provide a short-list of top-shelf online data science master degree options. This is an excellent time to move forward with your data science education as there have never been so many quality options that can result in long-term employment opportunities. Here are the top MOOCs for data science in 2020.

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As usual, Coursera offers many great learning options through affiliations with prominent educational institutions and companies. For instance, AWS offers some great courses through Coursera:

IBM also offers the following certificate series:

Google offers the following series of courses: 

Here is a shortlist of additional specialization course series:


edX is another very popular MOOC option that offers a number of quality certificate series in cooperation with well-known institutions and companies:


Although the Kaggle Learn micro-courses aren’t exactly new (they began in January 2018), the learning resources definitely picked up steam in 2019 due to increased interest from newbie data scientists, and also with some new course offerings. Given Kaggle’s continued prominence in the data science community (even after the company’s acquisition by Google in 2017), it is a natural place for data scientists to seek training. The courses consist of a number of short text tutorials with Python code examples (no video lectures). The courses use Kaggle Kernels which are essentially Jupyter notebooks in the browser that can be run interactively. The Kaggle learning resources are short and sweet and definitely a good starting point. Here is a complete list of micro-courses offered:

Online Degree Programs for Masters of Data Science

[Related Article: Big Fields Hiring Data Scientists for 2020]

Many up and coming data scientists are working to solidify their credentials in the field by completing Masters Degree programs in data science. Fortunately, in the past year, we’ve seen a number of new online programs pop-up to satisfy this rising demand. Here is a short-list of programs from well-respected institutions that you might consider (the programs vary in terms of admissions requirements, length of program and cost):


The path to a job in data science may vary. With the Ai+ Training Platform, you gain access to our massive library of data science training courses, workshops, keynotes, and talks. All skills are ideal for those looking to break into the field or to acquire the latest skills needed to get ahead. Some highlighted courses include:

SQL for Data Science: Mona Khalil | Senior Data Scientist | Greenhouse

Data Science in the Industry: Continuous Delivery for Machine Learning with Open-Source Tools: Team from ThoughtWorks, Inc.

How to do Data Science with Missing Data: Matt Brems | Managing Partner, Distinguished Faculty | BetaVector, General Assembly

Continuously Deployed Machine Learning: Max Humber | Lead Instructor | General Assembly

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.