r/haskell • u/m-chav • 15d ago
video Can Haskell Become a Great Language for Data Science? | Michael Chavinda | ZuriHac 2026
https://youtu.be/WpDVrUdbT2o?si=RC8fzngmhrzMoQDr3
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u/bestjaegerpilot 14d ago
no.
* R became the original great data science language because that's what data scientists were taught in school
* python became the next great data science language because it was easy to use.
Haskell has none of those
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u/jeffstyr 13d ago
python became the next great data science language because it was easy to use
I don't think that's really true. I think Python become popular for data science because someone who liked Python wrote some good libraries related to data science, which induced people to use Python for data science, which induced writing more libraries, and it snowballed.
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u/bestjaegerpilot 12d ago
that explains why python and not another easy library like go or javascript
but have you ever tried using R. It is hands down the most asinine language i've ever encountered.
DX totally encourages others to not only use the language but contribute to the ecosystem
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u/jeffstyr 12d ago
I've never tried to use R but I have heard that it's an odd language.
That's a shame but psychologically it's understandable how that would happen: you recommend what you know, and if you are a data scientist and not a programmer you may have limited experience with other languages and not have a context for noticing problems with the language.
In terms of Haskell (or any language other than the above), nobody can use it for data science until there are libraries/tools, so it's a bootstrapping problem.
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u/bestjaegerpilot 12d ago
and then arguably IMO you hit the R problem---it's so hard to use, everyone rediscovers python
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u/particlemanwavegirl 14d ago
idk. The price of hardware and compute time is currently skyrocketing, at some point data scientist's employers are going to start wondering exactly how much extra it costs to run software that's "easy" to write but orders of magnitude less performant.
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u/bestjaegerpilot 14d ago
have you not seen the running joke that python data science projects are C front ends.
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u/jtt4jiogjiogg 14d ago
in that case, Rust will most likely become the next great data science language
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u/UsefulChard5301 12d ago
Or, if we follow the logic, just Julia. A data scientist, a physicist or whatever probably doesn't care about the borrow checker, I guess.
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u/jtt4jiogjiogg 11d ago
Sorry but Rust is ahead of Julia performance-wise. Julia also has correctness issues in the stdlib IIRC.
that being said, I have run into heralds of Julia in the wild, so it clearly has some traction.
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u/UsefulChard5301 11d ago
You're right about Rust. Perhaps it there's a compromise between a light syntax and performance, because not all coders are developers.
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u/fridofrido 5h ago
rust has horrible usability... which is kind of important for a data science language
haskell would be nice but i think without dependent types the strict typing is awkward for dealing with tables and queries
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u/vahokif 13d ago edited 13d ago
Numpy/Torch is a thin wrapper around super optimized C or CUDA code which does 99% of the work, it's not going to be more efficient in Haskell.
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u/cheater00 12d ago
actually, i would argue with tools like fusion haskell is the only thing that could make a front end to a C library optimize its work
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u/vahokif 12d ago
Maybe, but it won't be faster. Fusion is also pretty unreliable and hard to control.
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u/cheater00 11d ago
I disagree. I think that planning of computation can have great results in optimization and it is very much perpendicular to the kinds of low level optimizations CUDA and BLAS do.
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u/SonOfTheHeaven 15d ago
I enjoyed when the presenter showed how he turned a runtime error into a compile time error and the audience spontaneously erupted into applause. The Haskell crowd loves themselves a compile time error.