MOOCs, Data Science And Free Software: The Story Of A Coincidence

Hamideh Iraj

Hamideh Iraj

MOOCs, Data Science and free software (as in freedom) have been three separate phenomena that happened to coincide in the last two years. MOOCs and data science were both hyped in 2012 and are growing up side by side. The time when MOOCs started to grow in terms of the number of courses, learning data science was gaining popularity and one of the MOOC platforms priorities became offering those courses much like what Coursera and Udacity did in courses and specializations.

In the meanwhile, instructors needed tools which were accessible to masses of course participants and can be taught without the license and copyright concerns. The simple answer was free software. There are large number of courses teaching R and a few others teaching Python. One upon a time, learning R was considered to be difficult with steep learning curve. It is still true but this has been moderated by the large number of courses teaching it. When you learn R in different courses, it is no longer that much difficult and you will become used to it. I have used student version of MATLAB that was offered by some courses such as Machine Learning by Andrew Ng but it was not the final solution. The use of Python in Data Science MOOCs has been limited probably because of the dichotomy over using python 2.X or 3.X. I think MOOCs are one of the ways to spread the idea of using free software. When people are learning free software, at least they consider it as an alternative in their usage in the real world.

On the Operating System side, things are different. Linux was not easy to put in MOOCs and still is not because it does not map to university courses and it is considered to be an industry skill. The only Linux MOOC (as far as I know) was offered by The Linux Foundation on Edx platform (excluding Udemy courses).

In addition to that, the use of RStudio, git version control, github website, stack exchange websites (which were formally introduced in Johns Hopkins data science specialization) and a bunch of other tools and services will increase as well. It would be interesting for me if any of the mentioned services could report if they have experienced a sudden surge in the number of users, downloads or hits after being introduced in MOOCs or not.

What do you think about these three topics interactions? Have you moved to a free software after completing a MOOC? Why Python is not taught in data science courses? Did availability of data science courses encourage you to learn it? How do you think teaching and learning at scale can change the dynamics of software usage?

Author Bio:

Hamideh Iraj is a big data and data science researcher. She writes on a wide range of topics including Big Data, Data Science, Information Technology, Education and MOOCs.

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