Julia to Give Data Scientists New Coding Powers

Its makers heralded it as the programming language for data scientists that will triumph all others.

Now with Julia 1.0 available, the race is on to unseat R and Python.

From the onset, Julia was designed for a “greedy” purpose.

“We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn yet keeps the most serious hackers happy. We want it interactive, and we want it compiled,” said an early note authored by Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman.

The needs are relevant for data scientists who often use Python or R to develop algorithms and then rewrite the code in C++ or Java to take advantage of processing speed.

Julia is designed to implement basic mathematical expressions used in algorithms fast.

Those who learn Julia will be able to conduct scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing easily.

So, should data scientists switch, after spending years learning the subtle nuances of other programming languages?

It depends.

Julia is still at version 1.0.

Other languages have matured and it remains to be seen whether Julia will develop faster to catch up or lag behind.

Julia also assumes that processing speeds matter, which is only true for large and sophisticated algorithms.

Many data scientists seldom work with such processing hogging expressions on a daily basis, although this is fast changing as they get involved in AI and complex analytical projects.

Community plays a significant role in a programming language's adoption rate.

R and Python have a strong following but Julia's community is only starting to grow. 

MIT's Julia Lab now supports Julia, and but it will need a bigger community to convince data scientists to switch over.

However, Julia is already creating significant inroads.

It already made the top 50 list at the TIOBE programming language index – a monumental feat in itself.

Will it replace the Python and R?

It is a problem statement that has very few data points at the moment.