Data Science tools: Are you “all in” or do you “mix and match”? – Strata

See on Scoop.itData Nerd’s Corner

An integrated data stack boosts productivity As I noted in my previous post, Python programmers willing to go “all in”, have Python tools to cover most of data science….

Carla Gentry CSPO‘s insight:

On the other end of the spectrum are data scientists who mix and match tools, and use packages and frameworks from several languages. Depending on the task, data scientists can avail of tools that are scalable, performant, require less2 code, and contain a lot of features.

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