> The decision to adopt or avoid a language is always a mix of their perceived formal power (“Does this language even have this particular feature?”), employability (“Will this language get me a job?”), and popularity (“Does anyone important use this language anymore?”).

I'm a statistician. I've always wondered what it's like to grapple with the question of what programming language to use. In statistics, the choice is very obvious: use R. If that's not fast enough, use Rcpp. This is definitely a good thing, because all academic statisticians in a certain age range speak R, so interfacing work is not so painful, but maybe a bad thing because statisticians don't really understand the pros and cons of many languages? If Julia blows up, maybe we will have to get smart on these differences.

Granted the workflow is probably the same in any language: clean data, model data, graph data.