People come to data science from a mix of CompSci, Business, and Statistics/Research backgrounds, often with some, but not all of the skillset. From what I've seen, it can be hard for people from any of those camps to to look into the other two camps and discern which of the skills are expected/important/relevant to a data science role.

This kind of article can be valuable as efficient diagnostics way to prioritize skills worth learning.

E.g. I'm from a business background, but regularly use SQL and Python. I know I'd need those to work productively in data science. But do I also need to know Git? Best practices to avoid SQL injection? What a monad is? How to implement Djikstra's?

I'm interested to learn all of those things, but I also have a personal life and an entire second list of know-I-don't-knows in statistics. It can be helpful to get confirmation that "yeah, you know enough SQL, but it's worth learning a little bit about graph traversal."