Julia is a high-level, high performance, dynamic, fast, easy to use, and an open source language. It is a general purpose language and can be used to write any application. It was invented by Alan Edelman, Viral Shah, Jeff Bezanson and Stefan Karpinski. Julia is the outcome of the brains who wanted a programming language to not be just a programming language that will lead the work only in some areas. Instead they wanted an all-rounder language that will lead and surpass all other programming languages. Julia's team wanted the work executed by this language to lead to an extremely different level and will be of great help and success in almost all areas of expertise in the diverse field of Computer Science and Numerical Analysis. Julia, in a single statement can be stated as ‘A fresh approach to Numerical Computing’ ; as the main motive behind inventing this language is to resolve huge statistics and traffic with speed and give the work a clean finish.

Advantages of Julia:

Julia is a versatile language with advantage of high speed like C, dynamic like Python, scalable like Hadoop, can analyze statistical and numerical data as R, familiar mathematical notations like Matlab and many more attractive features of other programming languages. In short, Julia is a language; a complete package of every virtue possessed by different languages, providing solutions to almost every challenge in data science and other real world problems.

Why are the data scientists using Julia?

With the rapid increment in the disparate and huge amount of data, a need for assemblage and proper usage of data has become a necessity in the professional life. As data is bulky and is growing every minute, data scientists are finding different ways to assemble it according to its content and quality, therefore, leading to the point where speed in handling of data is must. With Julia in data science, all the requirements are met and hindrances are resolved.

Why are the data scientists preferring Julia over Python?

Is Python losing to Julia? Well let’s see. Python, due to its dynamic semantics, has firmed its grounds in the fields for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. According to the Tiobe Index, Python topped as a programming language in 2018. But with the growing demand of Julia in the last 2-3 years, the position of Python in the work-life of data scientists will soon be surpassed by Julia. What Python can do, can be easily done using Julia that too with speed greater than that of Python- so why not Julia?. Here is a table of comparison between Julia and Python as stated in The IOT Magazine:

1. Comparison Table:

2. Trending projects or algorithms based on Julia:

There are so many projects going on using Julia. Scientists, coders, and researchers are expanding the use of Julia, trying out different ideas and algorithms. Some of the projects are:

Turing projects for probabilistic modelling and probabilistic programming.

Machine Learning for machine learning.

Scientific Machine Learning.

Compiler – work on the Julia compiler's internals to make things better for everyone.

High Performance and Parallel Computing – write code that runs on lots of machines, goes really fast, processes lots of data, or all three.

Numerics – Challenges for the hard–core number-cruncher, including linear algebra routines and basic mathematical functions.

Science – provide Julia with the ability for scientific research in various fields.

Differential Equations - Numerical methods for high-performance solving of differential equation models.

Images – extend Julia's suite of tools for visualization and analysis of images.

Graphs – extend the JuliaGraphs ecosystem with new algorithms and tools.

MLJ – a Machine Learning Toolbox for Julia.