Yes, this is where I think opinions diverge. I would say this: as a language “for developers and expert users”, Julia is definitely the best bar none. The reason is because once you get the hang of Julia, you can just use Julia without needing resources. Julia is developed really cleanly, employs very little/no magic, and the vast majority of Julia Base/packages is written in Julia. I find that the vast majority of the time when writing Julia, I can “guess” (or actually, just know) what the compiler is going to optimize and how it’s going to do it. I just check Base code and package sources to see how everything works instead of checking docs (and send PRs).
This is a style of using a language is something I hadn’t ever experienced before (years of other languages, about 1 year of Julia). In MATLAB/Python/R I had to always use lots of documentation, and search StackOverflow for answers. In Julia it’s usually unnecessary (the only time it comes up really is for actual Julia bugs, and usually I get a Github hit for what it is). Using C was too far in the other direction: isolated and re-inventing not just the wheel but also wood and stones and it was too much time wasted.
So if there’s a language to get really good at, Julia is definitely the right choice. That said, it is still easier “to be a noob” with Python and R since there are more pre-packaged solutions and StackOverflow answers ready for you. But even in Python/R/MATLAB, if you dig past the basics say to S4 objects and investigating what the compiler is auto-optimizing in the background, you quickly enter an area that is beyond what’s documented and answered (some of it may not even be well understood…).
This should be negligible for most problems which are not games (games only because of graphics drivers).