Chapter 5. How to Write Code

My experience of being a data scientist is not at all like what I’ve read in books and blogs. I’ve read about data scientists working for digital superstar companies. They sound like heroes writing automated (near-sentient) algorithms constantly churning out insights. I’ve read about MacGyver-like data scientist hackers who save the day by cobbling together data products from whatever raw material they have around.

The data products my team creates are not important enough to justify huge enterprise-wide infrastructures. It’s just not worth it to invest in hyperefficient automation and production control. On the other hand, our data products influence important decisions in the enterprise, and it’s important that our efforts scale. We can’t afford to do things manually all the time, and we need efficient ways of sharing results with tens of thousands of people.

There are a lot of us out there—the “regular” data scientists. We’re more organized than hackers, but have no need for a superhero-style data science lair. A group of us met and held a speed ideation event, where we brainstormed on the best practices we need to write solid code. This chapter is a summary of the conversation and an attempt to collect our knowledge, distill it, and present it in one place.

The Professional Data Science Programmer

Data scientists need software engineering skills—just not all the skills a professional software engineer needs. I call data scientists with essential ...

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