> 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?”).<p>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.<p>Granted the workflow is probably the same in any language: clean data, model data, graph data.