Last week, Peter and I gave a tutorial at SciPy 2016. In keeping with our recent thinking about reproducibility and organization, the idea was to look at how some of the hard-won lessons and best practices from modern software engineering can be applied to data science work.
We ran a little short on time near the end, partly because a few people ran into pretty wild open bugs and configuration issues during the virtual environment lab, and partly because we tried to fit a bunch of varied subjects into one tutorial. But overall, I think it ended up being a pretty good session.
The repo with slides, notebooks, and other materials is here. Here's the talk:
Any comments or suggestions? Let me know.