Wrong but useful. Working @Twitter, tweeting about data science and people management.
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i think calling that toolchain 'familiar' is the issue here -- there are many different paths, they aren't better or worse, but what feels familiar/simple/easy is different for people who have taken different paths, analytics people should get comfortable with thatpic.twitter.com/psQCakL2N9
in my experience, the people who tend to hate SQL most are coming from academia. this makes some degree of sense, since it's difficult to learn SQL without working for a company that has a real database to practice on
another possible way to think of this is methods-first (DS in this diagram) and business-context-first (analyst in this diagram). if your home base is the methods you learned in your academic training, it makes sense that you feel less fluent when you move to SQL
Wow, I didn't have this experience so far!! I actually find SQL useful because it's really easy to pick up, so many users from different backgrounds/teams can understand it. I just find it hard sometimes to write concise clean code. And I tend to go to R/Python if I want to test.
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- Reproducible 
And [this'll be a bit controversial, but] to get there:
- Use SQL when possible (vs Py/R).
- Don't share work primarily through git.
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