Quantitative biology workshop, MIT

MIT's 7.QBW's winding down. I'm not sure what to think about it.

On the one hand, it's nice to get to grips with some biological problems with the tools actual computational biologists use. As usual, MIT do things seriously, the syllabus is impressive, the lectures are great and stimulating, etc.

On the other hand, it's very frustrating − we only get to lightly touch on to some very basic concepts. I can't really say I've learned anything of substance; most of the “workshops” have been, by necessity, hobbled by the idea that primarily biologists would take the course, with little to no competence in computer programming. And so, to review a number of languages and tools in a very short time, everything is kept very basic, very introductory. Only once so far was there a really interesting problem (the MATLAB one on neurological analysis).

A quick trawl through the forums show that basically there are two populations − the biologists who find the programming assignments very hard, and the programmers who breeze through the course and are frustrated not to do more biology.

I guess the course's ambition makes it a bit of a tightrope exercise; maybe the MOOC format isn't so well suited to that kind of course? I guess people would have found it more satisfying if the course had been either an applied programming crash course for biologists, or a biology application aimed at software people. Then, by excluding one half of the prospective population, they could have gone deeply enough to teach actual skills and knowledge to the other half.

On the third hand, by applying standard expectations, we are reading this wrong. It's not a “course” and it's not meant to teach skills'n'knowledge. It's a workshop, based on an outreach program. It's aimed at giving people a glimpse of what can be done in the field of computational biology − and in that respect, I'd say it's pretty well hit the nail on the head.

But then again, it's pretty frustrating to get a glimpse of something cool and not have any way to reach towards it. If MIT created a systems biology course or sequence of courses, then this workshop would be the best possible introduction to it. On its own, it's kinda… unfinished.

PS oh, yeah, of course, I'm going to ace the course. But for the reasons explained above, I have no real merit in it.

Update 2014-08-02: yay, here's my certificate:

(Yes, I shelled out for the Verified cert, because it goes some way towards contributing to edX and MIT, and mostly because there are rumours that MIT Biology are considering creating an XSeries − then have verified certs in likely courses is a good way to get a leg up.)

Starting: 7.QBWx Quantitative Biology Workshop

Let's see… 7.00x Introduction to Biology got me hooked on to MOOCs. I've been assiduously taking courses in biology / medicine ever since, as well as computing courses and bioinformatics ones. So, when 7.QBWx was announced − a hands-on, workshop-oriented, introduction to the realm of “quantitative biology” (i.e. bridging the gap between data scientists and biologists), from the great people at MIT (were it not for the unfortunate Social Physics Buy-My-Book-Please-But-Let's-Pretend-It's-A-Course, I'd believe that MIT is the place where the exceptionally deserving go to when they die) − well, what could I do − except end this rambling sentence?

While the official start date was yesterday, the course hasn't really started − we've only been given a handful of introductory tasks to complete, basically checking that we could install all the necessary software on our computers. The real stuff begins next week. In the meantime, my freshly-updated Linux Mint is now equipped with R, Canopy Python (on top of system Python), Octave, and PyMOL.

I am slightly apprehensive: how can a 6-week workshop covering so many different tools and languages actually achieve any significant learning outcomes? The only way I can see is to make the course pretty hard − I'm not worried: by now I have a decent enough biology background, and of course I've been a professional programmer for 15 years, so I doubt I'll struggle in this course − which is my worry, really: if I don't struggle, how am I going to actually learn stuff?

Well, we'll see. I am fairly confident the great people at MIT (where the exceptionally deserving, etc.) have assembled a challenging, great course, and am eager to see what it comes to.

On a side note, a staff member (pseudonymed TurtlesAllTheWayDown, which − as a long-time Pratchett fan − I find absolutely wonderful) has admitted that an MIT Biology XSeries is at least being discussed. I am incredibly excited about this − and will therefore probably upgrade to Verified Certificate after next week, if it appears the course is really worthwhile, so as to make it count towards the future, possible XSeries.

15.071x The Analytics Edge is over; now what?

I had passed the, well, passing threshold a while ago, but it's always nice to get a high overall score. What I'm surprised about is my competition score, I thought I'd done quite poorly but I still got 82%.

So in the end, the final took me about four hours, and I made a number of stupid mistakes. Mostly because I'm absolutely knackered − I shouldn't, really, have attempted the final when I am well less than firing on all pistons, so to speak, but hey, aside from my amour-propre, there was no risk as I'd already secured a pass.

In total, it wasn't the most fun course I've taken, but it's certainly one in which I've learned a lot. While certainly not fluent in R, I have enough basic skills to fool around and try and analyze data; plus I now have a decent understanding of what data analysts do, which is a plus. While I'll never become an actuary or something like that, there is little doubt those basic skills will be of use in my future life.

So… this being out of the way, what now? This week also finish the Diabetes course from Copenhagen and the Statistics course from Berkeley; I've secured a pass for the first one, as for the second, edX's recent release reminded me that I registered as an auditor (because since I'd joined late, I didn't think I could actually pass the course − while I perfectly could). So I think I'll give the final a miss.

Ongoing courses are now: MongoDB M202, Epigenetics in Melbourne, and Astrophysics in Canberra. And that's all until June 2nd, when the Georgetown “Genomics Medicine Gets Personal” and the third part of Berkeley's Statistics course start, followed a week later by MIT's 7.QBWx.

But overall, the MOOC pressure is going down. I'll be doing 3 courses for the rest of May, 4 courses in June, then it drops back to 3 in July; August has only one course scheduled (Exoplanets). Compared with my current 6 (or 7, if you really want to count the joke that is Social Physics), it'll feel like holidays. Nice; I could do with a rest.

Fifo goes to eleven

In spite of an eventful week (including an unexpected couple of days in hospital; nothing serious though), my progress bar has just reached 58% on the Analytics course at MIT, where 55% is deemed a pass. So… that'll be my 11th MOOC certificate (9th from edX alone)!

I'd uncork the champagne, only I haven't got any at home (and anyway alcohol doesn't mix very well with the painkillers).