| There are mainly two types of data scientists, A and B [1]. Those B types are probably want to use Go for building data analytics pipeline similar to Pachyderm[2]. If you want to go the way of the compiled language for data science and numerical analysis the best bet now is probably Fortran. The fact that Swift for Tensorflow project was started and terminated recently really showed that there is a need for a proper and modern compiled language for data science and numerical analysis. There is, however, a dark horse in the data science and numerical analysis in the programming languages race that perhaps can satisfy both type A and B data scientists. The dark horse is D language. It supports functional, object oriented, borrow checker, inline assembler, REPL, metaprogramming, CTFE, open and multi-methods, just to name several modern features suitable for data science and numerical analysis but admittedly the eco-system is rather poor as of now (e.g. no library for Arrow). It also very fast to compile and run even with GC (the GC is also configurable) and you can selectively opt out for no GC inside the same code base if blazing speed is your things. But the glimpse of what it is capable of are there already albeit still in infancy compared to the mature languages like Matlab, R or Fortran [3][4]. But hey, Rome was not built in a day. [1]https://www.quora.com/What-is-data-science/answer/Michael-Ho... [2]https://www.pachyderm.com/ [3]https://tech.nextroll.com/blog/data/2014/11/17/d-is-for-data... [4]http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/... |
Or HPC languages like Chapel.
Not only they are compiled, they offer first class support for distributed HPC and GPGPU computing.
Go is nowhere close to offer such capabilities.