Honest question: what skills should a data scientist possess to graduate out of “shit tier”? Should we have all of the skills of statisticians, ML engineers, data engineers, software engineers, visualization designers, and domain/communication experts? Can it not be valuable to have some but not all of the above skill sets? Does it matter that software engineers are often “shit-tier statisticians” that understand just enough ML lingo to dismiss it as marketing hype?

I’ve gone out of my way over the years to make learning data science skills as approachable as possible for uninitiated (giving trainings, providing customized learning paths based on someone’s background, offering encouragement), and yet this is almost never reciprocated by engineer types. It’s always just, “data scientists can’t write production quality code”, with no explanation of what production quality entail, or without consideration of the fact that notebook-based data science can have advantages over perfectly modularized code with a battery of tests. See the comment above: “I'm not even sure what to recommend for developing good software judgment and habits.“. It’s like a chess coach admonishing their subject to simply “think harder”. Not helpful.

When curious and open-minded data scientists and software engineers work together, it can be magic. When people snipe at others for their “shitty” skills, it creates a petty and toxic environment.

This comment comes off as a bit of an admonition, but I would greatly appreciate a list like TFA for data scientists looking to shore up their fundamental CS and software development skills.

(PS — The first book I read when teaching myself R was R Inferno, so that ain’t it.)