Python or Julia? What’s Your Best Bet for Data Science

Both Python and Julia are considered as strong programming languages for data science professionals. While Python is a much older language and Julia the most recent one to become the preferred language for data science

Python! An admirable programming language and an excellent pick by tech giants like Google, Amazon, and Facebook are taking the data science industry by storm. Founded by the great Dutch programmer Guido van Rossum, Python is heralded to be a high level, powerful, and dynamic programing language used for the development of applications such as web apps, scientific computing, and prominently used for data analysis.

Whereas Julia officially entered the industry only in 2018, although it was unveiled to the programmers in 2012.

Julia was founded by Jeff Bezanson, Deepak Vinchhi, Viral B Shah, Alan Edelman, and Keno Fischer. The programming language was developed in an attempt to go far beyond just being between the developers’ stage, rather they wanted this language to be an expert in every aspect.

What’s the best pick for data science?

Although debates might say certain factors demonstrate Julia to be better than Python. But let us not get into the critics yet. Both Julia and Python have been established as great programming languages for data science professionals.

Why Julia?

Julia, a multiparadigm and primarily functional programming language was specifically built for machine learning and statistical programming.

Though new in the technology market, Julia is well-known for its quirky and unique features. And the coolest features include Julia’s multiple dispatches. We may further speak about the unique features as we move further ahead.

The invention of Julia by a four-member team was to address every kind of uncertainties and shortcomings Python programming language faces.