Back to school (sort of)

So the most rainy month of the year (in Paris − not that that's usual, mind, and not that I complain: I prefer wet Augusts to sweltering ones, to be sure) is drawing to a close. The academic year is starting, my former local sub-mayor is now education minister, and MOOCs are starting left and right. Not that they've stopped, really… Anyway, time for a sum-up:

Courses that are ending

Astro2, Exoplanets (Australia National University)

Well, Astro1 was great fun, Astro2 almost as much. I say “almost” not because the course itself is less good, but because by necessity it's a lot about the technology behind exoplanet discovery − not something I'm very interested in. Still, the staff at ANU made it a lot of fun, so there.

(It'll be my 15th edX certificate, 20th overall!)

The Emergence of Life (University of Illinois, Urbana-Champaign)

A big disappointment. Broadly, the course is supposed to be a quick run through the history of life as we know it (and not so much about it's emergence, really, but at least that's up front). The problem is it's dumbed-down, inaccurate, and the lectures are quite confused. I'm sticking to it because there are chunks I don't know about (“spot-the-fossil” and the skeletal morphology criteria for classification, mostly), but more often than not I find I'm shaking my head. It's less like a class and more like a 

No certificates here, they're not free and definitely not worth paying for.

A bunch of Data Science courses (Johns Hopkins university)

I know I wrote I wouldn't tackle the Specialization… but I had second thoughts, so I registered for Signature track on the first four modules of the Specialization, plus auditing the Statistical Inference one (which I had heard many people complain about, saying it's hard and obtuse.) Actually… once one does the projects and goes beyond the first week or so of each course, they're getting pretty good. The Statistical Inference course tries to run through a lot of unintuitive material in really too little time, but − after having done UC Berkeley's Introduction to Statistics course − I find it very interesting, very stimulating. I'm glad I'm only auditing it − this way when I take it “seriously” it'll be a review and hopefully by then I'll understand it better.

In any case, I expect four verified certificates to land in my pocket in the coming few weeks.

So if I'm counting right, I'm virtually the proud owner of something like 24 certificates. 20 obtained in 2014. Not bad… 

Upcoming plans

I've rearranged a bit my planning, ditching a number of accessory courses that I couldn't seriously fit along the rest. Still, next week is a busy one, with no less than 5 courses starting at the same time.

Explore Neural Data, Brown

Data analytics + neurology. In Python. Cool.

Fundamentals of Neuroscience part 2, Harvard

More neuroscience! Actually I'm mostly taking this one because I took the first part. Not sure I'll keep both neuro courses (then again, at the time I thought little of it, but after a while I find I keep using the concepts of Neuro 1; so it's been a good use of my time.)

Dino 101, U. Alberta

I don't expect much from this one, a lightweight dino course to pass the time.

Introduction to systems biology, Mount Sinai

I tried this a while ago and dropped it after a week, thinking it too hard. Hopefully, I've learned a bit since then, and MIT's 7.QBWx rekindled my interest in systems biology.

Introductory Human Physiology, Duke

No, I don't want to be a doctor. But yes, physiology and anatomy are interesting. This promises to be a heavy-workload class; we'll see if I keep it through.

Astro3, The Violent Universe, ANU

I can't stop halfway through the series! Sadly, it starts in October and I may be too busy to give it my full attention. Hopefully things will pan out all right.

Fundamentals of Immunology part 2, Rice

Ditto. I did the first part, which was great (though hard work), the timing of this second part isn't so good, but we'll do as we can.

Next

That's September and October pretty much spoken for. With luck, I'll be able to sneak in a Data Science course in there... I still have five full courses to do in the Specialization (Statistical Inference, Regression Models, Reproducible Research, Building Data Products, Practical Machine Learning). I've pencilled in one in October and two each in November and December. This way I still have some leeway until the next capstone project (expected in February).

I have also registered for the Open University's Start Writing Fiction course. Not sure I'll stick with it, but a writing class is interesting to say the least (even though English isn't my first language).