In my last post in this series, I spoke about what goes into a data science workflow. The current state of the art in data science is not ideal; the value of data is limited by our understanding of it, and the current process to go from data to understanding is pretty tedious. The right tools make all the difference. Imagine cutting a tree with an axe instead of a chainsaw. If you were cutting trees for a living, wouldn’t you prefer the chainsaw? Even if you only had to cut trees occasionally, wouldn’t you prefer a chainsaw, because, well, chainsaw! The key here is automation. Ideally you want as much of a process automated as you can, for the sake of productivity.