Being a list, sometimes with minimal explanation, and not to be taken entirely seriously. These might be influenced by living in Croydon, working in London and hanging around with people younger than myself who work in tech.
For many of these, I kept saying to myself “how did I not know about this before”. You might find them useful too. Others are true, but less didactic, and they are scattered like the proverbial Marvellous Aphorisms.
bootstrap.js – because every template I have used has ended up causing more trouble than it saved to begin with. I’m not a full-time web dev and I need something quick. It’s really easy. Do it.
Git + Atom with packages such as merge-conflict. Damn, this is good, but the obscurity of Git to most people is not going to go away any time soon. I had shoved some stuff cack-handedly onto GitHub but it was working with stan-devs last year and this year that really pushed me into using Git for version control in everyday work. You should really consider Atom if you use Git.
node.js (yeah, I had been dodging this too and feeling inadequate)
Why do academics not put some time, energy and budget into acquiring presentation skills? People keep telling me I am awesome etc, and I have really not done much to get awesome, so I rather doubt it, and it must be by association with others who are really bad. On a related note:
hipster slides. I mean, ditch beamer and powerpoint and all that crap and just put one massive black and white picture of a kitten on the screen. Preferably with one word across it in humungous letters, possibly a lurid colour. If you don’t have your own typeface designed for you (what a loser!) then use Helvetica (and by implication, do not stand up in front of anyone to talk with an obviously Windows machine unless it is done ironically). Try to have as few slides as possible. Like Van Morrison, I’m working towards having no slides at all. On another related note:
a lot of people are talking about improv classes as the key scientific skill of 2017. OK, nobody is, but there’s Alan Alda and @alice_data and Jeffrey Rosenthal, and the classic books by Keith Johnstone which are sitting on my shelf calling to me. I read them like a million years ago and I feel they might be even more relevant now.
finally started teaching myself Python. last year I decided to reduce the number of languages, whether scripting or programming, that I have to carry around in my head, so I could be less awful at all of them. I dropped Julia, although I think it will be brilliant one day soon. That meant that I needed to boost my C++ (I learnt some long, long ago) to get R speedy when required, and that had a few spin-offs. I expect the Python skillz will be important too in 2017, if my brain can accommodate it all. The eagle-eyed among you will notice I’m back to the same number I started with (groan).
The secret Stata 14 command
graph export myfilename.svg. Yes, SVG. God’s own graphics format. Just imagine what you could do… thanks to Tim Morris for spotting this. Goodness only knows why he was trying out file extensions for a laugh, or what else he tried that didn’t work. .123 anyone? But seriously, thanks StataCorp for taking this step, I know I have been droning on about it for years and now I’m really pleased with it.
Deep Work. You should seriously read this book. I now spend the start of each working day in a cafe of undisclosed location doing some deep work.
Ingrid Burrington’s work on internet infrastructure and what it tells us about secretive practices. Really eye-opening; you should get the book Networks of New York. I nearly lost my copy in the cafe of undisclosed location, but phew, they saved it for me.
Pinker’s Sense Of Style. Likewise.
Laura Marling, who I then listened to almost non stop this year. I’m not exaggerating. Perhaps responsible for the pessimistic tone creeping into recent writing on whether scientific practice will get better at replication, explanation and all that. More on that in the new year.
Rebecca Solnit’s “Field Guide To Getting Lost”. You’ll either get this or you won’t. If you do, you’ll be thanking me before long.
Mike Monteiro’s keynote talk at interaction ’15. Mentally find-and-replace designer with statistician and you have some important messages right there, plus a lot of swearing.
The Dear Data book, obvs
Cole Nussbaumer Knaflic’s book, which is the one I recommend now to viz noobs. It’s nurturing, if a little slow, and has the best coverage of perception issues that I’ve seen.
I read dataviz “classics” by Bertin and Wilkinson. Now I realise people talk about them a lot but haven’t actually read them, like Ulysses. The difference is I quite like Ulysses but these are just weird and not useful. Not good-weird, like EDA. You have to forgive Bertin a little for being a paid-up French semiologist of the 1950s, I mean it was his job not to say anything clear, but old Wilko seems to have written Grammar of Graphics while on a mind-expanding retreat.
Did a stack of reading around neural networks. They’re cool, and of course massively hyped. Feature selection and measuring uncertainty are the things to think about really hard before doing them. I’m doing NVIDIA’s two-day deep learning course in January ’17.
I decided that any complex set of predictor variables (without a clearly pre-defined subset based on contextual information) should be analysed in a number of ways, combining those from a traditional statistics training with those from a machine learning background: some kind of penalised linear model, some kind of tree, and some kind of non-linear feature combination. Maybe lasso, random forest and neural network. Consider boosting.
Did a stack of reading around AI. Interesting. A lot of compsci ML people seem to fly into a rage at the merest suggestion of killer robots (I can see where they’re coming from), and extend that to any ethical discussion (bad move, I think). You should read Nick Bostrom’s book (the ML guys hate it of course). Why does everyone assume it’s a bad thing to have humans wiped out by robots? We’re not really up to the job of running the planet. One thing I should write right now is that ML is not AI and statistical models like logistic regression do not really constitute ML either. You can relax for a few decades.
Every time I thought up some USSR – New Public Management – University life connection, I thought I was pretty damn clever, but of course Craig Brandist did it all before. What a guy. I bet they have a file on him.
I don’t like bananas, and come to that, cucumbers either. If I got to 42 and am still not sure whether I like a fruit, it’s time to stop trying. Likewise I expect to stop doing a lot of things in 2017, very much in the manner of Bilbo Baggins.