The Julia Programming Language: Uses, Benefits, and Careers

Key takeaways

Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman developed Julia for high-level, dynamic programming in scientific domains. They combined their favorite aspects of MATLAB, Python, R, and Perl to form Julia.

• Roles that often use Julia can offer strong earning potential. For example, data scientists earn an average annual salary of $112,590 [1]. 

• The Julia programming language attempts to combine the ease of languages like Python with the computational speed of C or Fortran. 

• You can use Julia in an array of disciplines like machine learning, data science, physics, engineering, and finance. 

Today, Julia is primarily known as a scientific programming language, and users often tout it as a language well-suited to computing with large-scale numerical needs. Explore the basics of Julia, its popular uses, and the careers you might pursue that need Julia. If you’re ready to build in-demand skills in data science, try the IBM Data Science Professional Certificate, where you’ll learn how to clean data sets, build machine learning models, program in SQL and Python, and build a portfolio in as little as four months. 

What is Julia?

This high-level, high-performance programming language offers speed and performance for technical and numerical computing. Colleagues at MIT developed Julia to address the limitations and trade-offs often encountered when working with languages like Python, MATLAB, and R, where users experience a trade-off between code readability and execution speed.

While these other scripting languages could support productivity, they proved slower when executing programs requiring high-level mathematics or technical computing. This lagging speed led many developers to migrate their code into another program to achieve their desired speeds. Yet, this could undermine efficacy. Julia came about to address this “two-language problem.” With Julia, developers avoid having to transition from one language to another.

Features of the Julia programming language

Julia is an open-source project known for its high performance and ability to generate highly efficient code at speeds approaching those of languages like C and Fortran. A dynamic language, Julia is relatively easy for programmers to learn and adapt. Although relatively young, since its release in February 2012, Julia has built a community of over 1,000 contributors.

Julia's multiple dispatch type system makes it easy to express object-oriented and functional programming patterns. This allows developers to write more generic code that can automatically adapt to different types of data, improving code reusability and extensibility. You can also use the Python, R, C/Fortran, C++, and Java libraries.

Julia also provides libraries for data manipulation, machine learning, analysis, parallel computing, and visualization, which are essential for scientific computing and data analysis tasks. 

Advantages and disadvantages of using Julia

When weighing the value of learning the Julia language, consider the pros and cons. Understanding Julia’s unique advantages and drawbacks can help you decide whether to invest your time and effort.

Julia programming pros

New and free, Julia combines programming simplicity with high-level capabilities. Other Julia programming pros include the following: 

• Computational speed

• New, advanced computing packages 

• Advanced features, such as multiple dispatch, differentiable programming, dynamic and declarative type system

• Homoiconic capabilities that facilitate metaprogramming

• Support for Python, C, and Fortran packages 

Julia programming cons

Nevertheless, you could also encounter some challenges working with Julia. Some potential cons to the language include:

• Difficulties when reading CSV files

• Some alternative features have design flaws that impact performance

• Slower compiling speed 

• Less community support than other programming languages

What is the Julia programming language used for?

Julia is particularly well-suited for tasks such as numerical simulations, data analysis, machine learning, and scientific computing, where performance is crucial.

Julia offers faster LLVM (low-level virtual machine) due to its just-in-time (JIT) compilation and better memory management, giving it impressive speed and math-friendly syntax. This has helped it gain popularity in finance, engineering, physics, and machine learning fields for its execution of: 

• Computing involving Algebra and other numerics

• Machine learning and artificial intelligence

• Data science

• Distributed and parallel computing

• Web development

• Organizational research

Companies and projects that use Julia

Globally, people use Julia to enjoy high-level capabilities with high efficiency. Institutions such as Stanford, Cornell, UC Berkeley, Brown, and MIT teach this programming language. Organizations using Julia include the following: 

• NASA

• IBM

• Microsoft

• Google

• Ford

• Pfizer

• KPMG

• Capital One

Julia programming jobs  

You can find several different types of careers that require an understanding of Julia. As an increasingly popular programming language, Julia’s scalability allows it to stand out as an option to meet the programming needs of numerous applications across different industries.

• Data scientist: Data scientists determine what data a project needs, collect and categorize it, and then provide clean, accurate, reliable data sets for analysis. They may perform analysis in some roles, using text and visualizations to explain the main findings. 

• Operations research analyst: Operations research analysts use data to identify problems in areas such as business, logistics, health care, or other fields. They collect and organize information from various sources, analyze that collected data, and report back the relevant information you can extract from the data.

• Computer and information research specialists: Computer and information research specialists innovate new and existing computing programs and information systems. They explore complex problems and develop theories of new ways to address them. 

• Database administrator: A data administrator manages an organization's data resources throughout their lifecycle. They design and implement databases, ensure data security and integrity, oversee data backups and recovery, optimize data storage and retrieval processes, and maintain data documentation and metadata. They ensure that data assets are well-organized, accessible, and aligned with business needs and regulatory requirements.

How to learn Julia 

You can learn Julia in many different ways. The official Julia website includes resources like Julia Academy, Julia Language’s YouTube channel, and a growing list of Julia tutorials. Other learning methods include the following:

• Exploring Julia documentation online

• Watching Julia tutorial videos

• Reading books on Julia

• Joining the Julia community

• Taking on basic Julia projects

Is Julia better than Python?

As a general-purpose programming language, Julia is not necessarily better than Python, but it does excel when it comes to high-performance computations compared to Python. Julia’s main design is for scientific and technical users to be able to harness the computational speed of C or Fortran while maintaining the ease of programming in a language like Python. 

How to download Julia programming language

One of the quickest ways to download Julia is to go to the official website download page at https://julialang.org/downloads/. The webpage automatically detects your operating system and gives you a command to run in your computer’s command line interface. 

The command for a Linux/MacOS system is: curl -fsSL https://install.julialang.org | sh

The command for a Windows system is: winget install julia -s msstore

Julia works with VS Code, Jupyter, Pluto.jl, Vim, and Emacs IDEs.

Discover free resources to advance your skills

Julia is a powerful programming language for data scientists, operational research scientists, machine learning, and high-level computations. Explore our free Career Resources Hub to find out which role in data science, machine learning, or data analytics suits your career goals. Then, discover our free resources for programmers:

• Subscribe to Career Chat: 6 machine learning certificates + how to choose the right one for you

• Watch on YouTube: R Programming Basics | Microsoft Tutorial

• Study terms: Data Science Terminology and Definitions | Coursera

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