Written by Aryan Sethi
Will Julia unseat the King of Programming?
Perhaps in some years from now, Python won’t be so impeccably sewed on us. Maybe that space will be taken by Julia.
Python, over the last two decades, has been arguably the “King of Programming Languages”. Its user-friendly data structure, open-source development, extensive support libraries, and portable and dynamic nature have been a few of the reasons for its utter dominance in the market. It is extremely popular among data scientists and machine learning professionals in particular and is extensively used for Artificial Intelligence. It seems to be surfing on a high tide which never ebbs.
But in 2012, a potential game-changer, Julia, entered the market. And its founders were greedy as hell. Seriously, I’m not kidding. That’s what even they said. They wanted an open-source language, the speed of C with the dynamism of Ruby (sometimes I wonder why so many programming languages have feminine names), something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl and as powerful for linear algebra as MATLAB. They wanted the best of everything for Julia (much like how parents want everything best for their children).
Below are the comparisons between Python and Julia in various fields:
Julia, as a programming language, is as fast as C. It was designed and developed for speed, as the founders wanted something fast. Julia is not interpreted, hence that makes up for a fast programming language. It is also compiled at Just-In-Time or run time using the LLVM framework. Julia gives you great speed without any optimization and handcrafted profiling techniques and hence is the solution for all your performance-based problems.
In terms of versatility, Julia, as a programming language, could do better. On the other hand, Python has more versatility. You can use Python for web scripting, automation, web development, etc. So, if you are looking for a general-purpose language, then Python is a better choice, and hiring Python developers could solve the purpose.
Julia is a dynamically typed language, it helps developers to create variables without specifying their types. Julia also provides the benefit of static typing. Python can also do the same except that it is not statically typed.
Both the programming languages, Julia and Python are capable of running operations in parallel. Python’s methods, however, require serialization and deserialization of data for parallelizing between threads whereas Julia’s parallelization is much more refined.
With the machine learning base of Python, Julia is simple like Python, and at the same time has big-data manipulation powers of Spark. For data scientists beginning their careers, this feature of Julia is something that must be taken note of.
Like every language, Julia too is being enhanced every day with ambitious plans. According to Keno Fischer, the future roadmap has “ML, various sorts of data analysis, image processing applications and taking advantage of modern hardware.” Julia is already widely used, with over three million downloads. Experts like Emmett Boudreau think that Julia has a lot of fun to write and would likely become even more viable for Data Science in the future. In this era of Artificial Intelligence and Machine Learning, languages like Julia have a bright and better future.
SHOULD YOU LEARN JULIA?
When it comes to data science, machine learning, and analytics, the demand for data scientists (and marketers) to know Python is high. Python holds the #1 spot on the May 2019 KDnuggets software poll, and will likely remain in the top place on the poll for years to come. If you’re new to data science and machine learning, it would be prudent to learn Python first. With that said, Julia has some advantages over Python that make the language worth checking out. If you’re already fluent in Python, learning Julia shouldn’t be difficult. Depending on your upcoming data science projects, it may be worth adding the Julia programming language to your repertoire.