Graham Wheeler's Random Forest

tl;dr This is a long post. I think its valuable reading, but I can also sum up my recommendations as: Build good but narrow APIs that are the public contracts for your code. Focus your tests on these. Don’t focus on testing at the level of methods, classes, etc, except insofar as these represent the public APIs, with the exception of complex algorithms that may need particular attention. Introduction This post is based on a talk I gave to my team in an effort to establish a common approach to thinking about automated tests: specifically those we would have labeled as ‘unit tests’.

I’ve been teaching a crash course in data science with Python, which starts off with learning Python itself. The target audience is Java programmers (generally senior level) so its assumed that things like classes and methods are well understood. The focus is mostly on what is different with Python. I teach it using Jupyter notebooks but the content is useful as a blog post too so here we go. The other parts are: