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Our RustConf talk, entitled The PlayRust Classifier, was essentially about how to reduce technical debt. It’s something that most software engineers experience regularly – the lack of documentation, unhandled errors, costly scaling, etc. However, technical debt in machine learning compounds quickly due to unique challenges in the space.

https://www.suchin.co/2016/09/13/The-PlayRust-Classifier/

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Rust Data Science is an attempt at bringing a solid basis for doing data science under rust. There already exist several scientific / data science library for rust but I'm not satisfied with their design. Following the famous xkcd joke, this is just another attempt.

https://github.com/haxelion/rds

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For the second year in a row, Rust was the most loved programming language. This means that proportionally, more developers wanted to continue working with it than any other language.

https://stackoverflow.com/insights/survey/2017/#most-loved-dreaded-and-wanted

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It seems as though most languages have some number of scientific computing libraries available.

* Python has Scipy
* Rust has SciRust

http://datascience.stackexchange.com/questions/411/best-languages-for-scientific-computing

This page should list the data-science crates, though at the moment the only one listed is one called cogset.

https://crates.io/keywords/data-science
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