In the dynamic field of data science, data scientists need to keep a track of the algorithmic development, languages, technology stack used, and databases. Julia is one such major development in the programming language, which earned considerable attention recently due to its unparalleled speed, and user-friendliness.
In this article, we shall deep dive into Julia, its growing significance, and its applications due to which it is worth learning this language in the Data Science career.
A breakthrough in data science programming languages – Julia is a general-purpose and high-level programming language that every data science professional can use for scientific computing.
Jeff Bezanson, Viral B. Shah, and Stefan Karpinski, the developers of Julia, came from various backgrounds and were keen on finding a language that unified the power of all other existing languages. This gave birth to Julia, a compiled language. The first version of Julia was released in 2012, and came live as an open source, with the speed of C, easy-to-learn, statistic-friendly, and general-purpose language.
Julia is the favorite among the crowd– Why? It has been downloaded over 35 million times as of January 1, 2022; also 3 times more often in 2021 compared to the previous three years taken together, according to JuliaHub.
Julia is compiled with the LLVM compiler framework to implement faster runtime efficiency through JIT (Just-In-Time) compilation. It has an interactive command line similar to Python where you can make code bits or one-off scripts with few key presses.
Julia can call libraries like C, FORTRAN, Java, R, etc. You can also call Julia from other languages through embedding APIs. It has the easiest syntax and a friendly debugging feature. Julia also supports metaprogramming, can rewrite the code, and create other programs which remind you of programming languages like Lisp. It has capabilities like parallel computing at every level: multi-threading, instruction-level, Distributed Computing, and GPU computing.
Many professionals use Julia due to its ease of use and stunning performance. These beginner-friendly and versatile languages have many applications in the industry; however, let’s take a glance at the most significant applications of Julia.
Data scientists, developers, economists, and analysts can use Julia to manage daily work. Julia is incredibly fast and has a great array of scientific libraries, making it the perfect alternative to R or Python. It has increased in popularity in recent years, and the tech giants like IBM, Apple, Amazon, Microsoft, and Google, already use Julia.
Though Julia is not best-documented like R or Python, experts suggest Julia will be the revolution of data science. They recommend learning Julia now and adding it to your Data Science certifications since it is going to be the next-big-thing in upcoming years.
However, some other data scientists throw a conservative opinion on Julia, with the opinion to learn Python and R first, since they are the most used programming languages across the world.
It is always good to learn a new language, which has scope to rebuild the future. Learning Julia helps you to streamline Machine Learning workflow and keep your portfolio unique which gives you a competitive edge in the job market.
Though Julia was developed for scientific computing, once it went live, it witnessed many applications in various industries. The TIOBE index for Julia is 0.64%, which means every 200th programming concerned query is about Julia.
NASA uses it in the migration patterns of humans, plants, and animals to record their response to changing climate. MIT uses Julia in robots, to enable their activities like walking on uneven or difficult terrains.
Julia is on the rise, but it still has a few miles to travel in terms of developing documentation, packages, community support, and libraries, however, the language makes a lot of milestones in the field of data science.
It minimizes the time-to-market in the places where the greatest challenge is the code execution time. Julia is ideal to use in applications where you use simple Machine Learning algorithms. It evolves steadily which makes it one of the best languages ever in Data Science. To fuel your career in data science with Julia and much-needed programming languages, make sure you learn them in your Data Science certifications.