I assume you mean, "over any other language I have tried" ?
As someone with a mathematical background myself, I am always surprised at how many data scientists and quants are ignoring more mathematically principled languages like F#, OCaml and Haskell.
Can I quickly prototype a new deep learning model and scale it to a 32 GPU cluster with very little effort in those languages?
What does it mean? Have you done it in any of those language? Have you seen it done in any of those languages?
I did. I'm doing a image processing recently and use OCaml for prototyping. I've tried python (I've used it a lot for that long time ago), I've failed, it felt to awkward. I've described my experience here [1]
If you have no experience whatsoever with ML family [2], and doing all the stuff in python, you'll most likely be much more productive with python of course.
But I find ML-like languages way more pleasant, and I'm far more productive with libraries like owl [3], which are more fundamental and don't have fancy stuff, and ML, rather than with python and fancy lib like numpy/scipy.
Also Julia could be a good choice hitting a sweet spot between fancy libraries and fancy language.
[1] https://news.ycombinator.com/item?id=20457505
If the answer is “no”, then it does not matter whether I’m an OCaml expert, because I’m still going be more productive with Python.
p.s. Julia is nice though, hopefully it will keep growing.
Which is of course a fair point. A language by itself is probably not even in the top 3 considerations when choosing new tech. Stuff like runtime, ecosystem and the amount of available developers would probably be more important in most cases.
Totally depends on a domain. In serious mission critical software you wont use libraries, but will use the language.
Because C++ and C are significantly better than Nim and Crystal.
There are also Ada and Spark and aerospace and very critical stuff.
> just don't find developers who know them easily
We don't look for OCaml/Ada developers, we hire programmers, and they program OCaml and Ada. It's not a big deal for a good programmer to learn a language, especially while programming side by side with seasoned programmers.
In my 6 years with Python, the only dissatisfaction with the language I felt was from parallel programming. I switched to Python from C, and at the time, I missed C transparency and control over the machine, but that was compensated by the Python conciseness and convenience. Then I had to dig into C++ and I didn't like it at all. Then I played with CUDA and OpenMP, and Cilk+, but I wished all that would be natively available in a single, universal language. Then I started using Theano, then Tensorflow, and now I'm using Pytorch, and am more or less happy with it. It suits my needs well. If something else emerges with a clear advantage, I'll switch to it, but I'm not seeing it yet.
As a bonus, it IS Python (numpy) in the background mixed with Scala. So you can use each language where they make the most sense - Python for the maths number crunching and Scala for the business logic and the architecture.
I think Spark also has .net bindings (so you can also tick F# on that list...).
There are a lot of reasons why this is.
#!/usr/bin/env stack
-- stack --resolver lts-6.25 script --package turtle
get_the_shit_done_quick ()
When you don't have such function, you need an expressive language to write it (and a bulk of python libs are not written in python, tho mostly for the performance reasons).
So it's all about finding a sweet spot between fancy libraries which do the shit for you, and fancy language, which let you to express things, absent in libraries.
This sweet spot differs from domain to domain, from user to user. Even in numerical stuff someone could have a requirement for a better language, although this domain is indeed to well defined to have enough fancy libraries.
To your original point of being "surprised at how many data scientists and quants are ignoring more mathematically principled languages like F#, OCaml and Haskell," I'd much rather use one of those languages, but I'd have to build the foundations myself. Today, they aren't the right tool for the job. They don't have the libraries I need, which means I don't build further libraries for them, making other people less likely to build on them, so they aren't the right tool for the job tomorrow either. I'd say it's a network effects thing primarily.