If you're interested in learning to code or are looking for a new language to learn, you may have heard of Julia. Julia is a relatively new programming language that has been gaining traction in the past few years. 

But what is Julia, and what can it be used for? In this blog post, we'll take a look at the history of the Julia programming language and some of its key features. We'll also explore some of the areas where Julia is being used today. 

What Is the Julia Programming Language?

Julia is a high-level, general-purpose, and dynamic programming language with great features for data science, scientific computing, and AI applications. Julia was designed from the ground up to be efficient and easy to use, and it has a rich set of libraries and tools for data analysis and visualization.

Julia's development took a fresh approach to technical computing, combining the ease of use of Python and R with the performance of C and Fortran. Julia was designed from the ground up to address the needs of high-performance numerical computing, and its speed is on par with C++ and Fortran. In addition, the Julia code provides a convenient syntax that makes it easy to express mathematical ideas. 

Julia is also open-source, so you can get started right away. And because Julia is designed for high-performance computing, it's a great choice for large-scale data analytics projects. So if you're looking for a powerful, easy-to-use, and open-source language, Julia is a great option.

The History of Julia

The Julia language was first introduced in 2012 by a team of developers led by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman. Since its inception, Julia has been used in various fields, including machine learning, data science, and scientific computing.

The language is named Julia without any particular reason, as one of the creators, Jeff Bezanson, has said in an interview. Julia was created to provide a single language that would be versatile and handle different tasks, unlike other technical languages used at the time for computing.

The design of Julia started in 2009, with the goal of creating a language that was open source, with high performance, and suitable for scientific and numerical computing. 

The language's design philosophy emphasizes programmer productivity and code readability. In addition to traditional imperative and object-oriented programming, Julia supports functional programming and metaprogramming. As of September 2022, the latest stable release of Julia is version 1.8.2.

Julia’s Features

While the Julia software was developed as a language for general use and technical/numerical computing, it's also well-suited for data science and machine learning applications. Some of the key features that make Julia a great choice for these tasks include:

  • Dynamic type system - Julia has a system in which types are associated with values rather than variables. This means that the type of a value can change during the execution of a program.
  • Multiple dispatch or multimethods - a type of polymorphism in programming languages that allows different methods to be invoked based on the runtime type of one or more arguments. It's used to implement traditional object-oriented concepts such as inheritance and interfaces but can also be used for other purposes, such as dispatch based on argument types.
  • Lisp-like programming language
  • Integrated package manager
  • Can function with distributed and parallel computing
  • Can manage processes through shell
  • Supports user-defined types
  • An interface that handles asynchronous input and output
  • Logging - a module that provides the means of recording the history and progress of a computation
  • Profiling - tools that help developers optimize their code by increasing performance. Each line of code is tracked, and the time needed to process it is measured to identify potential bottlenecks.

The Downsides of Julia Programming

While many features are implemented in the programming language, it's important to mention some things that Julia is lacking.

Julia is difficult to integrate into other programming languages. This results in increased latency and memory requirements for your code. Any code you write in Julia must be run through a Julia interpreter, which can be time-consuming.

The language is also not well suited for mobile development or web development. While Julia can be used for server-side scripting, there are better languages for these tasks, such as PHP, Ruby, and Python.

Since the language is relatively new, there are still bugs that need to be addressed. This can result in code that doesn't work as intended or unexpected behavior. However, the Julia team is actively working on fixing these issues, but it takes a long time.

The Julia programming ecosystem can't compare to languages such as R or Python. R boasts over 18,000 packages available, while Julia only has around 7,400. This number is increasing as the language grows in popularity, but it's still lagging behind other languages. 

If you are working in a niche field, you'll likely have to write your own Julia packages or just rely on learning R, which has a proven track record.

Despite these drawbacks, Julia is a powerful language that shows potential. It's easy to learn for experienced programmers and comes with many features that can be helpful for scientific purposes.

Ecosystems and the Julia Packages

So, why use Julia? The language is designed to operate in several different ecosystems and has several main applications.


Data visualization is one of the main features you would use Julia for. There are multiple packages to choose from with static and dynamic interfaces or ones with real-time updates.

General Purpose

With Julia, you can build stand-alone applications and user interfaces for the web or native apps and pull data from different types of databases. You just need to use PackageComplier for executables or HTTP.jl to deploy web servers. There are function interfaces for Fortran, C, C++, Java, Python, R, and many other languages.

Data Science

Julia users that want to process large data sets and manipulate databases can do so through DataFrames.jl and CSV.jl. Any results from your datasets can be visualized through the JuliaGraphs package. 

It's important to mention that Julia can work with a wide selection of databases, and it’s perfect for use in any data science job position.

Machine Learning

The Julia machine learning package includes an interface package that interacts with algorithms, such as decision trees, generalized linear models, and clustering. Julia also has packages for deep learning, pre-trained models that can process common tasks, and some of the best ones for probabilistic programming. 

Machine learning positions often require at least basic knowledge of Julia or any other similar high-level programming language. 

Scientific Domains

One of Julia's main strengths is processing differential equations and other scientific computing. DifferentialEquations.jl is a great example, as it can automatically select the best algorithms for solving problems. Other examples are Optim.jl, which has interfaces for nonlinear optimization methods, and much more.

Other ecosystems exist for specific fields, such as biology, image processing, quantum physics, ecology, astronomy, etc.

Parallel Computing

Programming with parallelism in mind is one of the main features of Julia coding. It can natively distribute computations across multiple processors and has built-in support for Message Passing Interface. There are also ways to parallelize code for GPUs, which is not as common in other languages.

Learning Julia

If you are not discouraged by some of the flaws we mentioned earlier, there are multiple ways you can learn Julia.

The Julia website has a great section for getting started. There you can find installation instructions, documentation, books, and videos that will get you on your way to coding Julia programs.

JuliaHub is a web-based IDE that allows you to write code in your browser without installing anything. This can be helpful if you want to test the language without making any changes to your computer.

IJulia is a Jupyter kernel for Julia that allows you to combine code, visualizations, and markdown, all in one place. Jupyter is a popular tool among data scientists, so this can be a great way to get started with Julia.

The Julia community is also very involved in developing the language. They hold multiple conferences every year where you can learn more about Julia and meet other people using it.