Julia is a high-level, high-performance dynamic programming language. It combines the ease of use of scripting languages like Python with the speed and efficiency of compiled languages like C/C++.
Julia has been gaining traction due to its speed, intuitive syntax, and ability to quickly and efficiently solve complex problems.
Being a general-purpose language, you can use Julia in many areas and it can perform various tasks.
In this article, we will be going through various areas where Julia can be applied. I'll also discuss the various packages you can use to get the most out of the language.
Julia Programming Use Cases
This is undoubtedly one of the widest applications of Julia. It is an excellent choice for Machine Learning, as it is highly performant and offers an extensive library of packages for Machine Learning Applications.
The MLJ.jl is a set of tools that provides a centralized interface to standard machine learning algorithms like clustering, decision trees, and generalized linear models.
Julia also provides deep learning frameworks like Flux and Knet, which makes it easier to work with neural networks.
Data Science and Visualization
Another strong application of Julia is in the Data Science field. Julia is well-suited for data science applications, as it is designed to be fast and efficient. It has a wide range of visualization packages you can use to create complex charts and diagrams and perform data analysis.
Examples of libraries include Plots.jl, Makie.jl for visualizations, UnicodePlots.jl for plotting in the terminal, and CSV to read CSV files.
The JuliaPlots GitHub organization contains several packages for Data Visualization in Julia.
Julia is not left out of the web development space as it has support to create web applications and APIs.
The Genie.jl framework is a notable example that includes all you need to build full-stack applications and APIs (Genie). It also helps you build interactive data applications (Stipple), No-code UI plugins (Genie Builder), and offers an ORM (Searchlight).
Franklin.jl is another example, and it is used to generate static sites. It is customizable. Examples of other packages include HTTP.jl, Dash.jl, WebSockets.jl, Mux.jl.
Julia offers powerful graphics libraries with a wide variety of visualization capabilities and interactive features.
Examples of graphic packages are Luxor.jl for drawing vector graphics, Javis.jl for making animations, Flux3D.jl, and Vulkan.jl.
With built-in components for parallel programming at every level, Julia was created with parallelism in mind. Julia provides in-built multi-threading to enable multiple tasks to be executed in parallel within a single process or program, multi-processing, and distributed computing to enable computers to take on larger tasks.
Packages you can use in this scope include LoopVectorization.jl, Dagger.jl, and DistributedArrays.jl.
Julia is suitable for robotics applications since it enables speedy algorithm testing and development.
For instance, the JuliaRobotics GitHub Organization offers a selection of tools for Julia robotics development. It contains 3D visualization, motion planning, robot control, and simulation libraries. The JuliaRobotics project also offers lessons and starter projects for those new to robotics in Julia.
You can also use Julia with a wide variety of current robotics libraries and frameworks, including Robot Operating System (ROS). Examples of packages you can use are RigidBodyDynamics.jl, Caesar.jl, and MeshCatMechanisms.jl.
Julia has a robust ecosystem of libraries focused on scientific computing and an effective inbuilt package manager to install and manage dependencies.
And bcause of its threading, distributed memory parallelization, and GPU computing capabilities, it is also making progress in high-performance scientific computing.
Packages like DifferentialEquations.jl for the differential equations ecosystem, JuMP.jl for Optimization and Operations Research, IterativeSolvers.jl for iterative algorithms in solving linear systems, and AbstractFFTs.jl for implementing Fast Fourier Transforms (FFTs) are available to use.
You can do audio processing, recording, development, manipulating, controlling, and other related tasks in Julia.
Some of the packages that support these abilities are WAV.jl, PortAudio.jl, and MIDI.jl. You can find other packages in the JuliaAudio GitHub repo. For Music libraries, there are several packages in the JuliaMusic GitHub repo.
From creation to designing, programming, and producing games, Julia can be a go-to for game development.
Popular packages include Starlight.jl, a “greedy application framework for greedy developers” for building mainly video games, along with GameZero.jl, and Nebula.jl.
There are other areas that show potential for Julia but that are still being developed. For instance, in the App/Mobile development area, it is still underdeveloped and immature. An example of an existing library in this field is GTK.jl, the Julia interface to Gtk windowing toolkit.
Industries Where Julia Excels
After looking at the several areas where Julia can be applied, let's learn about the industries and institutions where Julia can be practically used. Some of them include:
Banking and Finance
Julia can be used to create highly-developed financial models. Its libraries, like Plot.jl along with several others for data analysis and visualization, let you analyze and visualize market data and make decisions from the results.
Biology and Biotechnology
You can use Julia in the field of Biotechnology in several ways. For example, Julia can help you develop models that help predict the effects of specific treatments on biological systems.
You can also use Julia analyze big datasets obtained from biological experiments, create visualizations to help understand the datasets, and even develop algorithms. And you can use it to simulate biological processes and develop Artificial Intelligence applications.
BioJulia is an example of an organization that is in the Biology industry.
In addition to the languages used for Economic analysis like R and Python, you can use Julia in the Economics sector.
You can also use Julia for quantitative economics, to analyze data, and optimize problems. QuantEcon is a great place to begin the journey of Economics with Julia.
Julia is particularly suited for mathematical and scientific computing. It offers an extensive set of libraries to perform mathematical operations, including linear algebra, numerical analysis, Fourier transforms, and optimization.
Every computational second counts when it comes to climate modeling. Scientists can use Julia to quickly and easily develop data analysis and visualization tools, numerical solutions, and scientific computing applications.
Julia's numerical computing capabilities enable scientists to solve complex mathematical problems easily.
Medicine and Pharmacy
Julia is widely used in the fields of medicine and pharmacy research. Researchers use Julia to analyze large datasets to determine the effectiveness of drugs, understand the long-term effects of treatments, and simulate and develop new treatments.
You can also use Julia to create predictive models and to identify patterns in data. In medicine, you can use Julia to develop medical-grade simulations for medical imaging to study and analyze medical conditions. It can also be used to analyze medical data.
In view of the fact that Julia is very fast and easy to use, technological companies are staring to use the language more and more.
Companies and institutions like MIT, NASA, BlackRock, Pumas-AI, Pfizer, Microsoft, Google, IBM, and many more have adopted Julia for various tasks and projects.
In the energy sector, Julia can be used to analyze large datasets, develop models and simulations, and create applications for energy management and conservation.
It can also be used to develop energy forecasting models, simulate energy production and consumption, and create energy management applications. Additionally, machine learning algorithms can be developed with Julia for predicting energy usage and cost.
There are several other sectors where Julia can be used, like Sports and 3D printing. More including real life cases where Julia is used can be found in the JuliaHub case studies.
A variety of Julia GitHub repos provide libraries for different applications. You can find them in the Community Section of the Julia Language’s official website.
Overall, Julia is an excellent choice for those who want to maximize the results of their machine learning and data science projects.
It is a language for a range of applications because of its robust libraries, simple syntax, and fast speeds. Julia is a great option for completing tasks quickly and effectively, whether it's data visualization, data analysis, or machine learning.
Julia is versatile and resourceful and has applications in many fields. Julia is relatively easy to learn, and its scalability and ability to handle large data sets make it an attractive choice for many applications.
With its growing popularity, Julia is sure to become one of the more widely used languages for various applications in the future.
Special thanks to the people of the Humans of Julia Discord server for sharing resources in different channels and answering questions. Also attached are vital references for the article.