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A good rule to follow is to get it done dirty, add some tests, then refactor. Real-world code is not always pretty or academic quality.

Automation is also a good way to get rid of monotonous tasks and boilerplate.

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Does that work with data science work, though? Along the way you build many models and many kinds of ad hoc analyses that can build up. I’ve yet to see someone write tests. For the most part, I’ve only seen people write big long scripts that they call, setting some global constants at the top. I’m aspiring to be better than that, but it seems counter to the goal of getting results quickly.

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