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On The Future Of Statistical Languages

A couple of days after scheduling the “Data Science Wars: Python vs. R” post1, I’ve found this article by Seth Brown:

I’ve been thinking about the future of data analysis lately and which statistical language du jour will rise to prominence. On one side, there are languages built for doing statistics, which have some rudimentary programming capabilities, and, on the other side, there are languages built for programming, which have rudimentary statistical capabilities. This schism requires statisticians and scientists to be fluent in multiple languages, impairs the development of better tools, leads to feature duplication across languages, and generates needless technical debt.

After a much, much longer post than my comment, the authors’s conclusion is exactly the same: a general purpose programming language is more productive even if it lacks initially some of the advance algorithms and functions available in the highly specialized tool or language.

  1. Yes, for this time of the year I’ve scheduled a couple of posts in advance so myNoSQL would keep going even if I’d be traveling. 

Original title and link: On The Future Of Statistical Languages (NoSQL database©myNoSQL)