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Comment by evanpw | Hacker News Reader

If you only have time to learn one language, learn Python, because it's better for non-statistical purposes (I don't think that's very controversial).

If you need cutting-edge or esoteric statistics, use R. If it exists, there is an R implementation, but the major Python packages really only cover the most popular techniques.

If neither of those apply, it's mostly a matter of taste which one you use, and they interact pretty well with each other anyway.

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I'd say, if most of your job is analyzing the data yourself and trying to make sense of it, R wins hands down. Particularly if statistical graphics or advanced statistical methods may be needed, but it's still the case even if they won't.

If most of your job is going to be implementing data analysis techniques that you or someone else has done earlier and putting things into production, then Python will quite possibly be more suitable.

"If you only have time to learn one language, learn Python, because it's better for non-statistical purposes (I don't think that's very controversial)."

Actually, it is. When someone has only 3 or 4 years to finish their thesis and learning how to program is secondary at best, and they have to do it in a math-heavy department or field, there is no time or use to learn Python.

R does not mean only esoteric statistics. You have many more utilities in the R packages to diagnose and select models. Fitting a model is like 1% of the work, diagnostic is the more important part and R has much more to offer than Python ever will.

Statsmodels has tons of model diagnostic...and there is no R equivalent to Pymc3 (stan has less capability and worse API)

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