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Maybe a high-concurrency experiment runner or data flow engine, but Go would probably be the last "modern" language I think of as being good for data science. All of the features that make it great for writing high-concurrency web applications would make it painful for writing tabular data processing, array manipulation & linear algebra, and plotting. Nim seems a lot more practical; it's easy to bind to existing data science libraries, and you can use the macro system to build more expressive DSLs. That said, since Julia already does pretty much anything I would need to do (and will hopefully one day have a fast start up times and/or AOT compilation), I'm not sure why you would want to use Nim either. Maybe use it to write some kind of "mid-level" library code that binds to something like Torch, which you could then use from an even higher-level interactive language. Apart from the incumbents -- Julia, Python (grandfathered in + you can use Hy/Hissp/Coconut), and R -- maybe you could have a good time doing data science in Common Lisp or Racket. Again: good CFFI story, macros for expressive DSLs, flexibility to run in interpreted and compiled modes, dynamic/gradual typing for easy iteration, etc. Hell, I would sooner take Lua for data science over Go. That said, I am an "Arrow maximalist", because the beauty of it is that you should be able to use data frames even in Go if you really want to, without reinventing the CSV parsing and memory layout wheels. |
Similarly, Chibi or Gambit Scheme.
> I would sooner take Lua for data science
Which provides for a low level language like Terra or a Lisp via Fennel or Urn.