Monday, June 9, 2014

What would be good complements for Python and R?

No title
What would be good complements for Python and R?

by Christian Bolton

June 9, 2014


During 2014, I have been trying to expand and improve my computer programming skills, because of the long term benefits. I am not willing to do any more coursework or degree programs. I have already done far too much coursework, homework and exams.

Python and R are the 2 programming languages that I am convinced I should know how to use. I have already made some progress becoming familiar with Python and R.

My question is: which other programming languages would be a good complement for me to learn in the future?

There might be some objectives and contexts for which neither R nor Python is optimal. I certainly to do not know enough about computer programming to know which tool is optimal for which context and objective.

Would it be best to learn a programming language invented within the past few years, like Go?

Would it be best to learn the latest form of a programming language with a very long history, like Fortran 2008 or Common Lisp?

Would it be best to learn C only, or C++ only, or both C and C++?

My goal is to expand my long term raw computational power, in a way that is outside of academia and outside of all PhD programs and advanced degree programs.

I would especially like to be able to implement as many theories from statistics and numerical methods as possible. I do not know which theories from statistics and numerical methods I might find applicable in the future.

In case you want to know something about my academic background: I have done about 24 college level math classes, and zero college computer programming classes.


  1. For academia learn python and learn Scala. It runs on the JVM, where a lot of parallel computing is done. Skip R.

  2. Julia. High level but much better performance than Python or R. A very neat multiple-dispatch type system. Metaprogramming for when you want to do code generation. Smaller community and fewer libraries than either Python or R since it's much newer, but also much easier to contribute to the language, standard library, and packages since so much of the language implementation and high-performance libraries are written in high-level Julia. This is opposed to the internals of Python or R which are in C and Fortran (for parts of NumPy, SciPy, and R), so far less accessible.