# OCaml for Data Science

**URL:** https://discuss.ocaml.org/t/ocaml-for-data-science/1878
**Category:** Ecosystem
**Tags:** machine-learning, data-science, statistics
**Created:** [April 17, 2018, 11:56pm UTC](https://discuss.ocaml.org/t/ocaml-for-data-science/1878 "2018-04-17T23:56:36Z")
**Posts on this page:** 1
**Showing post:** 20

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### Author: ![mars0i](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ocaml.org/mars0i/32/1058_2.png) [@mars0i](https://discuss.ocaml.org/u/mars0i)
#### Post date: [May 2, 2018, 5:34pm UTC](https://discuss.ocaml.org/t/ocaml-for-data-science/1878/20 "2018-05-02T17:34:05Z")

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> [@bluddy](#):
>
> > [@mmottl](#):
> >
> > The issue with OCaml is that its greatest strength is also its greatest weakness: a powerful static type system. Given a reasonable set of libraries, many if not most practical data science problems can be solved within a few hundred lines of code. This is usually too small to really notice the benefits of static typing.
> 
> This is 100% right. Types show their strength when you have a large body of code and you want to be resilient to changes and refactoring.

I can’t argue, but: Static typing is still useful for small programs, and OCaml gives you that with minimal effort, especially if you don’t bother writing .mli files.

(Skipping .mli files seems to be slightly heretical. OCaml lets you choose, though. I love not having to annotate.)

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