Hint: There is no easy answer, and no consensus either.

Arguing about which programming language is the best one is a favorite pastime among software developers. The tricky part, of course, is defining a set of criteria for “best.”

With software development being redefined to work in a data science and machine learning context, this timeless question is gaining new relevance. Let’s look at some options and their pros and cons, with commentary from domain experts.

Even though, in the end, the choice is at least to some extent a subjective one, some criteria come to mind. Ease of use and syntax may be subjective, but things such as community support, available libraries, speed, and type safety are not. There are a few nuances here, though.

In machine learning applications, the training and operational (or inference) phases for algorithms are distinct. So, one approach taken by some people is to use one language for the training phase and then another one for the operational phase.

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