Julia is a high-level, dynamic programming language, designed to give users the speed of C/C++ while remaining as easy to use as Python. This means that developers can solve problems faster and more effectively. Julia is great for computational complex problems. Although MATLAB users may find Julia's syntax familiar, Julia is not a MATLAB clone. There are major syntactic and functional differences. The Julia language is considered quite a difficult language to learn because the syntax is not as simple as that of other languages. Also, there are fewer study materials than you may find for a language like Python or C, which have both been around for years longer than Julia.
Julia is faster than Python and R because it is specifically designed to quickly implement the basic mathematics that underlies most data science, like matrix expressions and linear algebra. Julia is already widely used, with over 2 million people having downloaded it, but the community of users has bigger ambitions. Julia is better than Python when it comes to memory management, both by default and by allowing more manual control of it. Given Julia's tendency towards being faster, making better use of multi-processing, and its mathematical appearance, many data scientists find Julia more comfortable and efficient to work with. Julia, an excellent choice for numerical computing and it takes lesser time for big and complex codes. Julia undoubtedly beats Python in the performance and speed category. The code at Julia runs at brilliant speed and is unmatched. However, lately, Python has become easier to speed up.
The negatives that Julia users report are that it's too slow to generate a first plot and has slow compile times. Also, there are complaints that packages aren't mature enough – a key differentiator to the Python ecosystem – and that developers can't generate self-contained binaries or libraries. The language is not worth your time if you're a beginner wanting to become a data scientist; it's better to go with Python as it's more widely accepted. But if you're an existing Python user wanting to expand your skills, you should definitely learn Julia and give it a try for numerical computation.
- Official Documentation On Julia
- JuliaCon 2015 Video Tutorial (Youtube)
- Julia Bloggers (Blog)
- Julia Tutorial By MIT (Youtube)
- Fast Track To Julia
- Julia: A Fresh Approach To Numerical Computing (PDF)
- Julia Scientific Programming (Online Course)
- Julia Language: A Concise Tutorial
- First Steps With Julia
- Interactive Tutorials on Julia
- Learn Julia by doing – First Steps with Julia
- Mastering Julia 1.0 (Packt)
- Getting Started With Julia (Udemy)
- Coding for non-programmers in Julia
- A gentle introduction to Julia
- Intro to Julia tutorial
- Julia language: a concise tutorial: Introduction
- Julia Roadmap - Internals & Design - JuliaLang
- State of Julia: the future looks modular, generic, and fast