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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
Got messaged by a C3 . ai recruiter. Read that wlb is bad and that the interview process is absurdly long, but the Glassdoor reviews are 4.2 and can't find actual hours worked posted by anyone. How's the culture really? I'd be aiming for DS consulting, something more functional but with DS/ML concepts as my differentiator.
C3.ai, Inc.
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Statistics and general data literacy is probably more important than learning more languages. Coding is a means to an end for data science
Tableau, PowerBI, and Alteryx are important tools too.
Nobody wants to spend all day in matplotlib.
It all depends on the stats knowledge and the scenario.
SAS, Stata, R? Every language has its strengths and weaknesses
You may want to clarify your question. If you are asking whether you need to learn other languages such as R or etc.. typically no but that will limit you to those languages. If you are asking that knowing python is enough to become a DS then the answer is no. There is alot of theory as well as practicall knowledge that you must gain to understand how to tackle problems.