What is Julia?
Julia is a high-level, high-performance, dynamic programming language. The language can be used to write any application, and many of its features are well suited to numerical analysis and computational sciences. Julie is garbage-collected, uses eager evaluation, and includes efficient floating-point math, linear algebra, random number generation, and regular expression matching libraries. Julia is often described as combining the power of C with Python’s rapid prototyping features.
The Julia project was founded in 2009 by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman, who set out to develop a free language that was also fast.
Who is Julia For?
Julia is popular with data scientists and mathematicians. There are common features with mathematical and data software like Mathematica, and its syntax is closer to the way mathematicians write formulas. Julia can also be used for big data projects due to its excellent support for parallelism and cloud computing.
Features of Julia Programming Language
Fast: From the beginning, Julia was designed for high performance. Julia programs are compiled into native code for multiple platforms via LLVM.
Dynamic: Julia is dynamically typed and feels like a scripting language. It also has good support for interactive use.
Julia environments that are reproducible can be recreated every time, across platforms, with pre-built binaries.
Composable: Julia uses multiple dispatch as a paradigm, which makes it easy to express many object-oriented and functional programming patterns.
Julia offers asynchronous I/O, metaprogramming, debugging, logging, profiling, and much more. Microservices and applications can be built using Julia.
Open source: Julia has more than 1,000 contributors and is an open-source project. The project is made available under the MIT license. Source code is available on GitHub.
Why learn Julia?
The Julia programming language, created by MIT, is quite popular among the developer community. Julia runs on a variety of platforms and is very versatile. It is also a fast and powerful programming language. Here are some of the main reasons why we must learn Julia programming:
1. Compilation: Julia is a compiled language, which is one of the reasons it performs faster than interpreted languages. Unlike traditional compiled languages, Julia is not strictly statically typed. JIT (Just In Time) compilation is used to determine the type of each variable in your code.
2. Solves the two-language problem: Developers tend to prototype their applications using slow and dynamic programming languages, and then rewrite them in a fast and static production language.
3. Technical computing is simple: Data Science is designed to help you excel at numerical computing. It is easy to access special characters with Julia REPL (Read/Evaluate/Print/Loop). The Greek alphabet, special math symbols, and subscripts.
4. Parallelism: Julia has a high-level syntax that makes it easy for programmers of any background to learn. With the Low-Level Virtual Machine (LLVM), Julia can compile native code efficiently for multiple platforms. Because of its primitives for parallel computing, this language is built for parallelism.
5. Support for other languages: Julia has a provision for using libraries available in other languages as well. The philosophy of Julia is “no boilerplate”. Additionally, the external call support interfaces with C++, Python, Java, and R.
The topic of Julia is well suited to help you get a job in the technical field. People who can bring a fresh perspective to companies are often in demand. Julia can be that fresh perspective! There are many opportunities in the Julia Ecosystem to make a significant contribution that you might not find elsewhere.