2013 was my first contact to MOOCland; 2014 is my first year of going full blast with Education 2.0. So far I've completed 14 courses this year, only one of them a carryover from 2013. I'm registered for an additional 13, and while I am almost certain to drop some, I'm fairly certain I won't stop there. As a comparison, last year I only completed 4 courses (6 if counting the two I audited); I've certainly gained some momentum there.
That's not the only difference. Not only did I get fully into this MOOC thing, I also have moved a bit from learning for learning's sake, and started focusing on a sort of pathway that may lead eventually to my career changing tracks. Which is funny, in a way, because I got onto the bandwagon to learn some economics theory, something that's never going to feature significantly in my work. Rather, I'm now gathering most of my efforts towards building a scientific curriculum centered around the general theme of computational biology. While my sense of fun is still my main driver in selecting courses, I've also come to pick up subjects in a utilitarian fashion, for instance taking all three Statistics courses from UC Berkeley (and the Analytics Edge from MIT) because I felt the need to brush up on the subject in order to better tackle the biology field.
Anyway; a half-year in review.
JANUARY saw the end of Harvard's slightly overproduced MCB80x - Fundamentals of Neuroscience. In retrospect, the course was better than what it felt at the time; I only wish there had been a quicker pace to it, and less time spent on cartoons and more on "test yourself" type exercises. (The instructor hates testing and grading − for reasons I can empathize with, while not really agreeing with − so this course has some virtual labs and a final exam, and that's it. No intermediate assignments, homework, etc. to fix things into the students' minds. Combined with a short-chapter-every-fortnight pace, that makes for a course that's very low-intensity and so, hard to commit to memory.)
It was also the start of CalTech's Principles of Microeconomics with Calculus, a very intensive course, very challenging, but quite rewarding too. I took it because, hell, I got into online ed to learn some econ, I wanted to swallow the whole pill. While the course ended up convincing me I didn't want to study economics for a livelihood (fat chance of me ending up that way even if I wanted to anyway) it managed to burn some basic economic concepts (supply, demand, monopoly, oligopoly, externalities, Pigouvian taxation, optimality, etc.) into my mind, which proves invaluable to my world-view generally. I haven't taken more economics courses since; when I get the motivation for it, there's the archived development economics course from MIT's Duflo and Bannerjee (both superstar economists in their own right) waiting for me.
In MARCH I started doing some serious MOOCing. It was the start of Berkeley's Stats course (which is both great and too easy − the contrast with their Californian neighbours at CalTech was terrible: CalTech's Rangel would have gone through the whole 15-week stats curriculum in, like, 5 weeks), that I took because I knew I was too ignorant in the way of statisticians for my own good; at the same time I started Rice's Fundamentals of Immunology (to keep doing some biology) and MIT's Analytics Edge (a very nice hands-on, reality-based complement to Berkeley's abstract, theoretical stats).
I also picked up MongoDB's DBA course at that time. It's a MOOC, running the edX software and produced in a very similar way to any of MIT's or Harvard's, but not offered by a university and very much focused on practical skills on a specific product, so I don't know if it really counts. I find it sociologically interesting though: the folks taking the course (or at least, those that were vocal on the forums) are very different from the MOOC-taking population on Coursera or edX. Let's be euphemistic and say it's a different mindset (in less charitable terms, a lot of people are only there to get a certificate to stick on their CVs and couldn't care less about the subject matter, or are plain stupid, or are whiny kids. Or all three at the same time.) Still, I admire the instructors' patience and I did learn a lot of useful stuff; never mind the forums.
Still in March, I started Copenhagen's Diabetes Challenge course (because my 4-year-old son has Type I diabetes and I'm very interested in the bio/healthcare sciences), which proved unequal but pretty interesting, Peking University's Bioinformatics course (which suffered from being dubbed in English by non-native speakers; it's awful to say that, but the delivery really hurt. Another big, big problem with that course is, well, honestly, how can you have an algorithmics course where you don't write a single line of code?) and ANU's first Astrophysics module (for no better reason than to get bragging rights, as in: "I studied under a Nobel prize winner"), which proved a blast (no pun on bioinformatics intended).
That was pretty much it (6 or 7 concurrent courses are pretty much my absolute limit) until the end of APRIL, when I started the eagerly-awaited Epigenetics course from Melbourne University, which picked up pretty much where MIT's 7.00x had left, genetics-wise, and was very, very good. Surprisingly difficult, because I don't know how to read scientific papers, really; but very stimulating. Around that time, I tried Mount Sinai's Introduction to Systems Biology and Harvard's Data Analysis for Genomics, but dropped both: they were too advanced, I was too tired. Instead, I refocused on finishing what I'd started (picking up Stat 2.2x 3/5th of the way through, and still managing an overall 65%) and cruised at 5-6 simultaneous courses until MAY, when a lot of courses ended. In retrospect, it's been my most fruitful month ever, as I picked up certificates for Analytics, Diabetes, Astrophysics, and Statistics (part 2).
May has also been the month of my biggest disappointment in MOOCland: while MIT's courses have generally been head and shoulders above the rest of the crowd, the Social Physics "buy-my-book" ad was a downright scam. I still don't understand how or why MIT and edX have let this pass, but hey, let's not take it against them and, well, be more selective in the future. It's a good reminder that the best can rub shoulders with the worst, I suppose, and that we should not take quality for granted.
On a similar note, I've been mostly an MIT/edX fanboy, because that's how I got into this MOOC thing, you know? But while I still prefer the edX platform overall (because of the more linear flow and richer interactive grading options, and despite the rubbish forums) I've gotten somewhat neutral. There are some great courses on Coursera too (starting with Melbourne's Epigenetics), and while the overall system feels more rigid, it's also less prone to bugs and delivers a consistently good experience − and the courses, well, they're often from less prestigious universities and tend to have less whizz-bang than the big kids at MIT-Harvard-Berkeley, but they're pretty good nonetheless. One just has to be more selective − and not hesitate to trial courses and drop the ones that don't fit.
JUNE has seen the end of Epigenetics and the start of a small bunch of courses: Georgetown's Genomic Medicine (which I feel ambivalent about: it's more an outreach program than an actual course. There are good things there, but no deep science − it's more of an extended documentary about the impact of genomic technology on the practice of medicine today. If nothing else, it reminded me of the difference between biological science and medicine, a difference that I didn't perceive fully twenty years ago, the reason why I opted for maths/physics rather than biology in my formal education), MIT's 7.QBW (a great, though frustrating, glimpse of what computational biology can be, which motivated me to try again Mt Sinai's Intro to Systems Biology in September), ANU's second Astrophysics course, this time about Exoplanets (more because it's fun and stimulating than because the co-instructor has a Nobel, this time), and the last part of Berkeley's Stats program.
At the end of JULY, 7.QBW is finished, Genomic Medicine is in its dying throes, Exoplanets is rolling along, and I've just started U. Illinois' Emergence of Life, which feels… I don't know, haphazard? Anyway, it's as good an introduction to evolutionary biology as I'll get in the summer − MOOCs are going slow until September.
So… What next? Well, I'm registered for a whole bunch of courses. First, I'm trying out the UK's own FutureLearn platform to learn about the Scottish independence referendum (my grandfather was Scottish, so I feel kind of romantically attached to the land of Ayes and Scotch, although I've only ever been there as a tourist). The course straddles the referendum itself, from Aug 25th to the end of September, so we'll get to learn about the issues then about the aftermath (although it's pretty clear the No will win). I don't expect this course to take up much of my time.
Much more seriously, come SEPTEMBER I'll be taking:
- Data Analysis and Inference from Duke University - a more practical (with R) overview of statistics. Not very high on the priority list, more like a way to keep the stats knowledge warm.
- Physiology from Duke University also - something I intend to put quite a lot of hours into. Medicine without the practice-of-medicine angle, just the thing for me. (For the summer I've downloaded a 1500-page textbook on Physiology from OpenStax, in order to get a heads-up).
- Exploring Neural Data from Brown. A follow-up on MIT's Quantitative Biology, this time focused on neurology and Python. I have high hopes for this one.
- Introduction to Systems Biology from Mount Sinai. Hopefully this time I'll be able to follow the professor along; I'm still convinced the subject is very interesting, despite the professor being, hmm, clearly a much better scientist than teacher.
- Explore Statistics with R, from Karolinska Institutet. Just because I want to hear Swedish accents again − and okay, because it claims to teach where to get good healthcare-related data too. I guess I'll skip the parts about learning R.
- Introduction to Dinosaur Paleobiology (Dino101) from Alberta University. I'm a 37-year-old kid, so what? Anyway, everybody says the course is enjoyable but low-intensity, which is fine with me, what with all the other courses at the same time.
In OCTOBER most of these courses will still be running (all except Scottish Independence and Explore Stats) but I'll still be starting Delft's Engineering for Bio-based products, because I feel it's my continental duty to pick up some European courses (what do you mean, I'm not convincing?) and because it's somewhat intriguing. Also, by the end of the month starts the second part of Rice's Fundamentals of Immunology, that I'm pretty committed to seeing through (I've done the first part and I hate leaving things halfway done).
Sometime in Q3 2014 (whatever that means) starts Harvard's Muscoloskeletal Anatomy − a good complement to Duke's Physiology, with virtual labs (including dissections). Also, if I see the Systems Biology through, then I'll probably embark on Experimental Methods in Systems Biology and the rest of the specialization.
So, that promises to be quite an eventful second half of the year. We'll see how it turns out!
On a side note, I'm starting to wonder about turning all this knowledge into an actual degree and a career change. There seems to be options with the CNAM (France's continuing education university-like institution, aimed at working professionals) so I'll be contacting them in September to study things through. Paradoxically enough, if I do end up taking evening classes there, it'll be because of MOOCs − but it'll also mean I can't take MOOCs anymore for time reasons. Oh well, that's all very hypothetical. We'll see!
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