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Huh, so it turns out that I've been a 'data scientist' for over 20 years. Who knew?
Even worst, most of the tools (cat, grep, awk) are Unix commands, redeveloped by the GNU project in most of the GNULinux distros.
Good introductory article here!
I was lucky that my first job was as a support engineer at a data-centric tech company, which is where I learned these. I've often thought about how to teach them to data analysts coming from a non-engineering background. This is comprehensive but clear and would be a perfect resource for training someone like that. Thank you!
[1] https://news.ycombinator.com/item?id=17324222
P.S.: Not essential, but it really becomes a joy when, as a touch typist, I have turned on vi mode in the shell (e.g., with 'set -o vi'). My fingers never have to leave the home row while I do my shell piping work from start to finish. (no mouse, no arrow keys, etc.)
Huh, so it turns out that I've been a 'data scientist' for over 20 years. Who knew?
That was my first thought skimming through this too. Either every *nix admin who is aware of a few text processing tools is a data scientist, or “data scientists” are just as full of it as I’ve expected.
The whole point of this article is to point out that a lot of common Linux tools can be used for Data Science like work (a significant part of which includes pre processing structured and unstructured text).
Can somebody explain the advantage of doing it on the command line vs in Python or R? What would a practical use case look like?
Rememeber, nearly all cases where you have:
cat file | some_command and its args ...
some_command and its args ... file