Python To Julia For Data Scientists

A quick guide on moving your Data Science projects from Python to Julia.

For nearly two decades, Python has exploded into mainstream popularity, primarily driven by an increasing demand for insights, computer-based Science, and Data-Science. While Python remains the dominant force in this Scientific programming-language ecosystem, beyond the bounds of Python exist a plethora of alternative options that accomplish the same or similar goals in different ways. There are several burgeoning languages that I think are also worth learning or learning about in the Data-Science sphere. One of the most unique languages on this list is the Julia programming language.

Julia is a relatively new and unique language in a novel paradigm. One of the best ways to diversify your capabilities as a developer is to force yourself to think differently. While these varied experiences will certainly come from a career programming in a single language, learning more languages in more programming paradigms is a far faster and more efficient method to becoming a more-rounded developer. This is especially true for languages that reside in majorly different programming paradigms. I discussed this thoroughly in an article I did earlier this year: learning more languages makes you a better developer.