[stared]

I would shift priorities to Python (from SQL).

Unless one has "data scientist" title so to make "database engineer" look more fancy, then data comes in various shapes and forms. And most questions cannot be answered with a simple aggregation.

For example, data I work on (I am a data scientist freelancer) is flat csv files, xls files, JSON files, some text files I need to parse, various SQL, MongoDB, things I am getting from various APIs, etc...

While understanding joins is crucial (and normal forms, etc), SQL itself does take negligible amount of my time (and effort).

[S4M]

I would disagree with that advice. If you work as a data scientist in a company, you will likely have the logs of something stored in an SQL table (be it pure SQL database or something like hadoop hive) and you will have to answer (and ask) to questions like: "Do people convert more when they come from X or Y?", so you will have to do a couple of queries to get the conversion rates from people coming from X and Y.

This is my experience when I worked as Data Scientist about a year ago. Now, YMMV, especially if you're a freelancer, I guess your clients are more comfortable with giving you raw dumps of data as files instead of giving you access to their database servers.

[stared]

I work as a freelancer. And actually, I never ever processed logs.

Of course, sometimes I am given SQL access to server; but I never learnt SQL except for in action (i.e. things which I need right now).

And most of times I work with flat files. Even if they come from SQL they typically need a serious preprocessing before I can do a more adv analysis.

BTW: I have no problems with composing rather advanced queries. Just if SQL is a problem from someone (and, in case of doubt, it can't be Googled in no time) then I am curious how can get machine learning.