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2. https://github.com/fsprojects/awesome-fsharp#data-science
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I think OCaml is more practical and actually has some parallels to Python. You may find these resources interesting:
- Library to manipulate PDF documents: https://github.com/johnwhitington/camlpdf
- Web framework: https://aantron.github.io/dream/
- Scientific computing library: https://ocaml.xyz/
Last, I wrote a couple of practical-focused OCaml guides:
- https://dev.to/yawaramin/practical-ocaml-314j
- https://dev.to/yawaramin/practical-ocaml-multicore-edition-3...
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How did you select those two as your options?
I'm just a hobbyist that enjoys programming, and I eventually wanted to expand beyond python. I looked at Haskell and read Learn You a Haskell and did some Exercism exercises but never got anywhere close to being able to use it for real projects. Have been trying to learn about Lisp lately and feel like I've come to a similar dead end.
On the other hand, both Go and Rust have felt fulfilling and practical, with static typing and solid tooling, cross compilations, static binaries, and dependency management that is just a huge breath of fresh air coming from python.
The ML / data science scene is nowhere near as developed as in Python, and I still lean on jupyter/polars/PyTorch here, but I think the candle project[0] seems very interesting. Compiling whisper down to a single CUDA-leveraging binary for fast local transcription is pretty cool!
[0]: https://github.com/huggingface/candle
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Learn You a Haskell For Great Good! is also a really good resource:
https://learnyouahaskell.com/
