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Five Engineering Skills Every Data Scientist Should Learn
Rounding out tactics to help you stay competitive as a “full stack” data scientist
As somebody who enjoys mentoring people to their fullest potential, I’ve had the sincere pleasure of mentoring many undergraduate students majoring in data science. What astounds me is how little engineering tactics are taught as a part of these programs. From students attending state schools to even Ivy League universities, I constantly hear that the emphasis is placed on pure data science skills. While these skills aren’t wrong by any means, it leaves a gaping hole in making a data scientist into a “full stack” data scientist.
By “full stack”, I don’t necessarily mean things like learning web development. What I mean specifically is being able to make your predictive model usable in a production setting. It’s one set of skills to know how to build the model; it’s another to know how to make it usable by others!
Fortunately, it’s my opinion that this is an easier thing to learn than pure data science work itself. You don’t necessarily need to be an expert in any of these skills, but having a foundational level of knowledge is important nevertheless. Depending on the company you end up working for as a data scientist, there may very well be the expectation that…
