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You still need to know SQL, and algorithms are helpful unless you expect to be spoon-fed pristine datasets that are ready for analysis. The applicant pool seem

You still need to know SQL, and algorithms are helpful unless you expect to be spoon-fed pristine datasets that are ready for analysis. The applicant pool seems to be full of those who took an online course and whose "personal" projects consist entirely of projects ripped from fast.ai or similar. Anymore, I get seem to get more spam on LinkedIn from people looking to be hired as a data scientist at my company than I do from recruiters looking to hire me. And looking over their resumes, I can see why they need to hustle so much. Successful candidates need to know how to do more than classify pet images from the Oxford dataset. So to answer your question...Maybe? I mean, these candidates are certainly trying much harder than I ever needed to for a job. But presumably they eventually get hired into roles with less discerning companies.

webartifex 7y ago
Agree. I actually found that I set up a Postgres instance to put the data of an object and use pandas only via sqlalchemy. Most of the stuff pandas does, can be achieved a lot more efficient if done right in a real database. I also agree about the algorithms part. For my research, I look into vehicle routing problems a lot and there is no sklearn for that or something alike. Maybe an idea for a future project. An experienced data scientist with deep knowledge in the topics you mention easily replaces 10 of those "candidates". I feel the best way to train more serious programmers and data scientists is to teach kids to program earlier in high school. Maybe make it mandatory just as math is today. Then, a lot more students may choose CS or math as a major in college.