Wrong but useful. Working @Twitter, tweeting about data science and people management.
"Wait, it's all her views?" 
"Always has been"
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Analytics work needs to be two things to scale well:
- Discoverable 
- 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.
Read more
, and fight me! 
https://towardsdatascience.com/how-to-scale-your-analytics-org-by-ditching-git-3d8d4ce398d1 …
If you get into any fights, I will join your side. the SQL-by-default take is probably the spiciest thing in here but it’s a take I very much agree with. So much of DS work is about counting, and SQL is hard to beat at that, particularly with modern data warehouses
But why would you use such an impoverished language when perfectly good abstractions can sit on top?
what sort of abstractions are you thinking @RussellSPierce?
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