Also in bioinformatics, and I use Python too, but not for the same reasons. Among languages with mature statistics/ML/numeric libraries, your choices are R and

Also in bioinformatics, and I use Python too, but not for the same reasons. Among languages with mature statistics/ML/numeric libraries, your choices are R and Python (MATLAB/Mathematica if you're willing to consider non-open-source). R is just terrible, so Python is all that's left if you want a sane language with batteries. I used to write a decent amount of Clojure. I would be surprised if you would have problems with reviewers complaining about a nonstandard language -- unless you're publishing a library, then you might rightfully get dinged because the target audience would be small. But if you're just running a routine analysis, I have always found the reviewers don't care what language it's in. Anyway, I dropped Clojure because the numeric support is just terrible. There's Java libraries, of course, but you lose the conciseness and functional aspect. You just can't performantly use arrays or matrices as if they were generic sequences, as appealing as it appears from a distance. Considered Racket, but I consider lazy evaluation a must (a la Python's generators), and I could never figure out how to use Racket's lazy mode. So, Python it is... Maybe choosing a programming language is like marriage: better to have something you can tolerate for a long period with all its warts than something you're infatuated with. > I have a way to justify my usage of Lisp in the future Haha, OK. I found this paper really unconvincing. No mention of the terms "numeric" or "array", and the only mentions of "statistics" and "machine learning" are in the form of "Lisp would be a really great foundation for stats/ML if someone would just write a library for it".