I gave a talk at last year’s Pydata London, which was one of the key-factors in starting a dialog with O’Reilly and the eventual book-building odyssey. So I owe the Pydata team more than a few shout-outs. No matter how stressful this authorship thing is, it’s an interesting ride. And having to explain things to other people is a great way to up your game. Anyway, just like last year’s conference, the atmosphere was really friendly and unintimidating, and the Bloomberg offices a very impressive and highly functional setting. As an ex-academic research-scientist I’ve done a lot of conferences, and many were rather dry and sober affairs. The Pydata-London guys and gals work hard to keep things upbeat and inclusive – Pydata is a charity and that is very obvious in the decidedly un-corporate feel. So I was genuinely privileged to be there. The talks were of a very high quality too (how mine got through I’ll never know 😉 – with three parallel streams, I had to miss a couple that would normally be bankers. Everything was videoed so I hope to catch them soon on the Pydata channel.
The talk built up to the Nobel visualisation I’ll be using to teach D3 – and a big shout-out for Maria Goeppert-Mayer, the only woman other than Marie Curie to have won the Nobel prize for Physics.
The slides for my talk are available here
and the visualisation on slide 42 here
Select category:Physics and gender:female to see a woman who should be far more widely known.
I saw some great talks, but particularly enjoyed Russel Winder and Ian Ozsvald’s friendly banter during Russel’s talk ‘Making Computations Execute Very Quickly’ (http://london.pydata.org/schedule/presentation/48/). The talk was more of an interactive coding session but I give big props to anyone prepared to risk that. Mr Winder knows no fear. Back in the day I worked in Evolutionary Robotics, evolving Artificial Neural Networks as robot-controllers. That takes a lot of processing metal and so the talk brought a lot of fond memories back, of trying to push just a few more bits out of a recalcitrant CPU. Russel made a few good points about not relying on the Numpy cut-and-paste to save you. But, as Ian pointed out (and Donald Knuth would agree), and as is stressed in his Performance Python book – first profile the life out of those bottlenecks. And as Donald Knuth would also point out, the choice of algorithm is a far greater factor than anything else. So grok your basic big-O notation.
Once again, thanks to the Pydata-London team for another great conference. And the growth of the regular Pydata London talks has been astonishing – this is a huge area right now, with some really exciting developments and it’s a privilege to be associated.
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