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I'm a data engineer and avid Racketeer. Racket is not at all a satisfactory replacement for Julia if you are doing data science. Specifically if you use Flux, you should know deep learning tools are basically non-existent on Racket. Someone once made a paper called DeepRacket and a Github repo, but it hasn't been touched since.

If someone out there knows more than I do please let me know because I'd love to be wrong. There is some decent linear algebra stuff, at least: https://docs.racket-lang.org/math/matrices.html

Racket is both helped and harmed by it's popularity in certain academic circles. Harmed, in that a lot of graduate students make a thesis of creating some badass library in Racket only to completely abandon it. But I'm still really rooting for Racket, it deserves more hype.




> Racket is both helped and harmed by it's popularity in certain academic circles.

Exactly, in a few ways. One way is that a number of Felleisen's original grad students got professorships and kept doing systems research (and development) with Racket. But most contributors who don't get professorships, nor the handful of researcher jobs, end up working on other systems where the jobs are, academic or industry. Industry use tends to be one-person projects, often because you have a smart person doing something that can't be done with off-the-shelf (e.g., Naughty Dog's video game narrative DSL, a think tank researcher's work, some complicated data science server stuff that I worked on, and various indie moonlighting projects), and so one-person efforts aren't posting jobs. Bigger industry use would mean more contributors, but top programmers are generally ill-equipped for enterprise sales, so I'd bet on startup success stories to jumpstart greater popularity (even if you use Racket to beta, and then whatever you figured out gets rewritten in a popular commodity-worker platform).




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