Having programmers do data science is terrible, if only everyone else were not even worse. The problem is of course tools. We seem to have settled on either: a bunch of disparate libraries thrown into a more or less agnostic IDE, or some point-and-click wonder which no matter how glossy, never seems to truly fit our domain once we get down to it. The dual lisp tradition of grow-your-own-language and grow-your-own-editor gives me hope there is a third way.
This talk is a meditation on the ideal environment for doing data science and how to (almost) get there. I will cover how I approach data problems with Clojure (and why Clojure in the first place), what I believe the process of doing data science should look like and the tools needed to get there. Some already exists (or can at least be bodged together); others can be made with relative ease (and we are already working on some of these); but a few will take a lot more hammock time.
Built my first computer out of Lego bricks and learned to program soon after. Emergence, networks, modes of thought, limits of language and expression are what makes me smile (and keeps me up at night). Currently working at GoOpti making the company data-driven; setting up our analytics infrastructure (end goal: provide any answer stemming from data in 2 min or less); and building our predictive-real time-superduper pricing engine.