Will Julia Replace Python and R for Data Science?

For those of you who don’t know, Julia is a multiple-paradigm ( fullyimperative, partially functional, and partially object-oriented) programming language designed for scientific and technical ( read numerical) computing. It offers significant performance gains over Python ( when used without optimization and vectorized computing using Cython and NumPy). Time to develop is reduced by a factor of 2x on average. Performance gains range in the range from 10x-30x over Python (R is even slower, so we don’t include it. R was not built for speed).

Industry reports in 2016 indicated that Julia was a language with high potential and possibly the chance of becoming the best option for data science if it received advocacy and adoption by the community. Well, two years on, the 1.0 version of Julia was out in August 2018 ( version 1.0), and it has the advocacy of the programming community and the adoption by a number of companies (see https://www.juliacomputing.com as the preferred language for many domains — including data science.

While it shares many features with Python (and R) and various other programming languages like Go and Ruby, there are some primary factors that set Julia apart from the rest of the competition:

Advantages of Julia