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.
