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Benjamin Obi Tayo, in his recent post "Data Science MOOCs are too Superficial," wrote the following:
"Most data science MOOCs are introductory-level courses. These courses are good for individuals that already have a solid background in a complementary discipline (physics, computer science, mathematics, engineering, accounting) are trying to get into the field of data science."
And he's right. There are a lot of introductory level MOOCs out there, competing with one another for introductory level learners. Which is all well and good, if that's what you are looking for. But what if you are a more advanced learner? What are your options then? Are there even valid advanced level MOOC offerings?
As Tayo points out, there are lots of alternatives to MOOCs for learning data science. There is certainly no reason to treat MOOCs as the go-to option for any reason.
However, beyond the relative predominance of introductory level offerings, there also seems to be a movement dismissing the validity of MOOCs for more advanced purposes outright, and there are 2 main reasons for this:
First, there is a prevailing assumption that everyone taking a data science course is someone with absolutely no prior related experience or education, and a related assumption that everyone believes that they can become a data scientist after a single 4 week course.
Second, there seems to be a belief that an inordinate number of people are taking MOOCs solely for collecting the related credentials, and the related assumption that everyone believes that this credential collecting will inevitably lead to a successful data science career.
If you disregard these assumptions, you can simply see MOOCs as a potential learning and skill acquisition tool for students of varying degrees of pre-existing skill. Given that many MOOCs are put together by world class instructors and educational institutions, there seems to be a good chance that one can learn something of value from these courses.
With that in mind, this article will look at a few advanced data science courses for those who already have a solid understanding of some data science foundational skills. What is advanced? Well, it's admittedly subjective, but I do my best to curate relevant courses of an advanced self-classification. What are data science foundational skills? Also subjective, and in this case they will vary, depending on the particular advanced course being considered.
To narrow the vast array of MOOC offerings, I had to come up with a filtering criteria. For our purposes, a valid MOOC is one which:
Point 1 omits, notably, Udacity, and other related pay-only platforms. Point 2 omits a platform like Udemy, which is open to anyone for course hosting. Point 3 allows, theoretically, for the logical chaining of courses to build up in-depth related learning concepts over time.
This criteria, more or less — as far as the major powerhouse MOOC platforms — leaves us with Coursera and edx. Note that we could have extended this definition much further beyond this criteria, or considered MOOC platforms, but this compiled list is intended to give some ideas about advanced courses as opposed to being an exhaustive curation.
If you are beyond the basics of data science and are looking for quality advanced MOOCs to further your knowledge, check out these 4 offerings.
The Advanced Machine Learning Specialization from the National Research University Higher School of Economics on Coursera is a collection of 7 courses on a range of machine learning topics.
This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
The courses in this specialization include:
The Probabilistic Graphical Models Specialization is a collection of 3 courses from Stanford's Daphne Koller hosted on Coursera.
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
The specialization consists of these courses:
The MicroMasters® Program in Artificial Intelligence from Columbia University and edX is made up of 4 courses and provides a deep dive into artificial intelligence.
Gain expertise in one of the most fascinating and fastest growing areas of computer science through an innovative online program that covers fascinating and compelling topics in the field of Artificial Intelligence and its applications. This MicroMasters program from Columbia University will give you a rigorous, advanced, professional, graduate-level foundation in Artificial Intelligence. The program represents 25% of the coursework toward a Master's degree in Computer Science at Columbia.
The 4 classes that make up the MicroMasters program are:
The Reinforcement Learning Specialization from University of Alberta on Coursera is a 4 course collection covering the use of reinforcement learning to solve real world problems. Here's what you'll learn:
Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.
By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science.
The courses involved are:
We hope you enjoyed this list of the most extensive MOOCs available for data science and machine learning. Be sure to follow us on Twitter and subscribe to Hacker Noon for more of our posts!
This article was written by Matthew Mayo, an Editor at KDnuggets.
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