I recently finished grad school and accepted a data scientist job a couple weeks ago in part because the job description mentioned expertise with Julia as one of the preferred qualifications (it's rare to see a listing that mentions Julia). It's the language I used for the bulk of my research over the last four years, and has been improving rapidly since it was released. I like Julia a lot more than Python, and I hope it continues rising in popularity. I think once it hits v1.0, we'll begin to see a lot more companies adopting its usage for data science, statistics, and machine learning.
> I think once it hits v1.0, we'll begin to see a lot more companies adopting its usage for data science, statistics, and machine learning.
I don't. It doesn't have the incumbency of R, the use in other areas of programming of Python, or a company actively marketing it like MATLAB. It's not 5x or 10x or whatever good enough than the alternatives to assert itself in the playing field.
If it means you get your work done using it, be all means use it. But I think it will stay around clojure levels of use in data science, statistics, and machine learning.
Once you can compile a julia app- front end, back and probabilistic prog/ML to web assembly and have it run in browser and mobile, it will skyrocket in popularity.
Why? The only current benefit of Julia afaict is the tracing jit. If you run the tracing jit on web assembly then it's giving up most of its performance benefits. And Python could be built on web assembly as well.
But who knows. Weird things seem to become popular despite all the negative points.
Forgive me, it's not a tracing JIT but just LLVM's JIT.
Precompiling in Julia is extremely not-straight-forward. You would think you just use --compile and it would work; but it doesn't at all.
Also, at ~850kb, Python's runtime is not that hefty. It's intended to be embedded and while it's quite a bit larger than lua's 200kb, but smaller than libjulia's 16mb.
