This was my first massively online course that I finished, to completion.
What were my thoughts? Mixed.
To be honest, it was hard to stay motivated throughout. The course also suffered from a difficulty ramp, first 2/3 was easy, then the last 1/3 hits you hard. Difficulty level overall was pretty for low me.
The finger exercises were great because they give you immediate feedback on whether what you learned in the lecture was sufficient.
The problem sets suffered from one major problem. In addition to the difficulty ramp, if for some reason, you have trouble passing the input vectors for the first part of the problem set, then you were toast for the rest. That was frustrating. A frustration which could have been mitigated if I had spent time connecting with other students in the discussion forums.
Honestly, the greatest thing about this course is that I gave no concern whatsoever about the grade I'd eventually receive. Instead, I focused on learning what I could. What a refreshing and liberating feeling.
What I got out from the course was a introduction (or re-introduction) to some CS topics that weren't properly covered during my EE/CE studies. The subject material was presented using Python, which I thought to be a wonderful.
The course succeeded in it's goal of teaching the basics of data science. I now have a better understanding of stochastic programs / monte carlo simulations (using randomness in computations), data visualization and curve fitting using pylab, knapsack and graph optimization problems, and machine learning - well, sort of for that one.
I personally learn best from working on open ended problems, not contrived examples. The course could be improved by adding such a component.
As a final note, because of the lack of an individual project component, there's a good chance I'll forget most of the details of what I learned :) The redeeming factor is, without question, the wonderful textbook that I can go back and refer to. The process of sitting through all the lectures and trying (but not completing) all the problem sets made it such that my copy of the textbook is nicely highlighted throughout. This will make it easier for me if I ever needed to write code to, say..., apply machine learning to making sense of functional coverage data.