John Myles White

Julia’s Role in Data Science

Myths and Realities


Since its first public release in February 2012, the Julia programming language has received a lot of hype. This has led to some confusion about the language’s current status. In this post, I’d like to make clear where Julia stands and where Julia is going, especially in regard to Julia’s role in data science, where the dominant languages are R and Python. We’re working hard to make Julia a viable alternative to those languages, but it’s important to separate out myth from reality.

Where Julia Stands

In order to the dispel some of the confusion about Julia, I want to discuss the two main types of misunderstandings that I come across:

  • Confusion 1: Julia already possesses a mature package ecosystem and can be used as a feature-complete replacement for R or Python.
  • Confusion 2: Julia’s compiler is so good that it will make any piece of code fast – even bad code.

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