A lot of DataSci is academic adjacent. So DataSci tools are optimized for academics, not production.
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Imagine if core file IO functions were spread across 5 different libraries. That's the status of datasci right now.
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There is just no good reason for PCA to be in scikit-learn and SVD to be in numpy. They're the same damn thing modulo a symmetrization op.
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I've wondered the same thing. Any hints of an effort to rationalize the ecosystem?
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Seems to address only the numerical corner of the ecosystem, but otherwise cool.
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As an academic "go read the docs" can make me want to punch someone in the nose. Halfassed docs & uncommented code abound.
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Oh. *Now* you tell us?
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You're right, but as a engineer looking in, DataSci gets this methodological rigor from academics that my type's fuzzy on.
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Adapting to the tools is challenging, but it feels even harder to be sure we're drawing the right conclusions from data
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I find myself Googling a lot to see what other people did in similar situations and replicate that - the engineer way :-)
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