In the highly disruptive world of data science, nothing lasts forever. Every time new tools and software pop up, they have the potential to challenge the technology status quo. This dynamic is particularly visible in the programming language landscape. There is a good number of programming languages well-suited for data science. While the data space is currently dominated by a handful of very popular programming languages, other new, allegedly more powerful languages are gaining momentum. One of the most promising examples is Julia. Released for the first time in 2012, Julia can be considered a data science rising star. Despite its relative newness, Julia has already impressed the world of numerical computing with its high performance and unbeatable computing speed. Over the years, Julia has grown in maturity, and today it is commonly labeled as one of the future languages of data science and artificial intelligence. There are many compelling reasons to start learning Julia, especially for those already versed in scientific computing. The transition pathway may differ depending on the programming languages you are familiar with. While most articles and courses focus on how to transition from other popular languages, it is also important to provide resources for users from other programming languages interested in progressing to Julia. The goal of this extensive series is to introduce Julia to MATLAB programmers. We will study the differences and similarities between the two languages and provide a detailed guide to migrate from MATLAB to Julia for data analysis, visualization, and high-performance computing.
Understanding MATLAB’s Enduring Legacy
MATLAB is a programming language specifically designed for numerical computing. Since its launch in 1984, MATLAB, which is an abbreviation of “MATrix LABoratory,” has been widely adopted in academia and scientific research. It was revolutionary for its time, providing scientists and engineers with an interactive environment that treated matrices as fundamental data types, eliminating the need for complex memory management and boilerplate code common in languages like Fortran or C. This focus on matrix mathematics made it an incredibly efficient tool for linear algebra, signal processing, image analysis, and control systems design. Despite being relatively old, it’s still an extremely popular programming language, maintaining a high rank in various programming language popularity indices. Its longevity is a testament to its power, the robustness of its toolboxes, and the deep roots it has established in university curricula and industrial research and development departments worldwide. Millions of students have learned programming and numerical methods through MATLAB, making it a lingua franca for many scientific disciplines.
MATLAB’s Strengths in Academia and Industry
MATLAB language excels in high-level computational tasks, including advanced mathematical and statistical operations. The language also includes powerful, integrated plotting and algorithm implementation capabilities, making it a perfect candidate for data science, engineering simulation, and algorithm prototyping. The true power of MATLAB lies in its extensive collection of toolboxes. These are add-on libraries of functions, often highly optimized and rigorously tested, that provide specialized functionality for specific domains. Whether it is the Signal Processing Toolbox, the Image Processing Toolbox, the Control System Toolbox, or the Statistics and Machine Learning Toolbox, these add-ons allow professionals to implement complex solutions with minimal code. This integrated environment, where computation, visualization, and programming are seamlessly combined, creates a rapid development cycle. An engineer can conceive an algorithm, model it, simulate its performance, and visualize the results, all within a single, cohesive platform. This is why it remains a favorite in fields like aerospace, automotive engineering, finance, and medical research, where domain-specific tools and proven reliability are paramount.
The Limitations of a Proprietary Environment
However, MATLAB also comes with an important caveat: it is a proprietary tool. This means that, unless your affiliated institution grants you access, as is often the case in academic settings, you may have to pay a large amount to get a license for the core product and for each additional toolbox you require. This cost can be a significant barrier for individual developers, startups, or researchers at less-funded institutions. Furthermore, since users don’t have access to its source code, it can get hard to understand how some of MATLAB’s core capabilities work. While the documentation is extensive, the inability to inspect the underlying algorithm for a specific function can be a hurdle for debugging or for advanced users who need to understand the precise implementation details. These factors can make MATLAB less attractive compared to other programming languages that you can use for free. The open-source movement has fundamentally changed user expectations, with many developers now preferring tools that are transparent, extensible, and free from licensing constraints.
The “Two-Language Problem” in Scientific Computing
For decades, the world of scientific computing has been plagued by what is often called the “two-language problem.” Scientists, engineers, and data analysts prototype their models in a high-level, dynamically-typed language like MATLAB or Python. These languages are easy to write, read, and debug, making them ideal for exploration and iteration. However, when it comes time to deploy these models into a performance-critical production system, or to run them on massive datasets, the relative slowness of these interpreted languages becomes a bottleneck. The solution has traditionally been to rewrite the performance-critical parts of the code in a low-level, statically-typed language like C, C++, or Fortran. This “two-language” approach is inefficient and error-prone. It requires a development team to be proficient in two completely different syntaxes and programming paradigms, introduces a new source of bugs during the translation process, and significantly slows down the time from research to production. This gap between prototyping and production has long been accepted as a necessary evil in the field.
Introducing Julia: The Ambitious Newcomer
This is the exact problem that Julia was designed to solve. Behind any innovation is the desire to improve or outperform the dominating technologies of the time. This was the driving force behind Julia, which draws inspiration from some of the most powerful programming languages. As the founders of the language stated in a famous post explaining why they created Julia, they wanted a language that was open source, with a liberal license. They wanted the speed of C with the dynamism of Ruby. They wanted a language that was homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like MATLAB. They wanted something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as MATLAB, and as good at gluing programs together as the shell. They wanted something that is dirt simple to learn, yet keeps the most serious hackers happy. They wanted it interactive and they wanted it compiled. This ambitious vision was to create a single language that could bridge the gap, solving the two-language problem once and for all.
Julia’s Unique Approach to Performance
Because of its unique features, resulting from this cherry-picking process that brings together the best of the best programming languages, Julia is rapidly gaining momentum. Its core innovation is its compiler. Unlike MATLAB, which is primarily interpreted, Julia is a compiled language. However, it is not compiled in the traditional, ahead-of-time (AOT) manner of C++. Instead, Julia uses a Just-In-Time (JIT) compiler built on the powerful LLVM framework. When you run a Julia function for the first time, the compiler analyzes the specific types of the arguments you provided and generates highly-specialized, efficient machine code for that exact combination of types. This machine code is then cached, so subsequent calls to the same function with the same argument types are executed at native C-like speeds. This means you get the flexibility and ease-of-use of a dynamic language, but without the typical performance penalty. You can write your code in a high-level, MATLAB-like style, and the JIT compiler handles the hard work of optimization, often achieving performance that rivals or even exceeds hand-written C or Fortran code.
A Rapidly Growing Ecosystem
Since its first official release, Julia has been downloaded millions of times, and the Julia community has registered thousands of packages for community use. These include various mathematical libraries, data manipulation tools, and packages for general-purpose computing. This ecosystem is crucial for its adoption. While it does not yet have the decades-spanning breadth of MATLAB’s toolboxes, the Julia ecosystem is growing at a much faster pace, driven by an active and enthusiastic open-source community. You can find mature packages for differential equations, optimization, machine learning, data visualization, and, of course, data frames and statistics. Because Julia is open source, these packages are developed in the open, allowing users to inspect the code, contribute fixes, and propose new features. This collaborative development model fosters rapid innovation and ensures that the tools are robust and cutting-edge. As for its popularity among all other languages, Julia has been steadily climbing the ranks in programming indices, showing a clear trend of growing interest and adoption.
Why MATLAB Programmers are Uniquely Positioned
MATLAB users are, in many ways, the ideal candidates to learn Julia. The transition is significantly easier for a MATLAB programmer than for someone coming from a language like Python or R. This is by design. The creators of Julia were heavily inspired by MATLAB’s syntax, particularly its clean, mathematical notation for linear algebra. You will find that array manipulation, indexing, and many mathematical function names are remarkably similar. For example, both languages are one-indexed, meaning the first element of an array is at index 1, not 0. This single detail removes a massive source of bugs and cognitive friction for MATLAB users. You can write a for loop, define a function, or multiply matrices in a way that feels immediately familiar. However, this similarity is a gateway to a much more powerful and modern language. You will be able to leverage your existing mental models for numerical computing while gaining access to a more expressive type system, superior performance, powerful metaprogramming, and a fully open-source ecosystem.
Core Comparison
When considering a transition from a long-established tool like MATLAB to a modern challenger like Julia, a direct comparison is essential. MATLAB users are accustomed to a certain workflow, a specific feel, and a predictable set of performance trade-offs. Julia, while syntactically familiar, operates on a fundamentally different philosophy. This part will dissect the practical differences between the two languages, moving beyond the high-level introductions from Part 1. We will explore the “why” behind Julia’s speed by contrasting their compiler technologies. We will then dive deep into the syntax, highlighting the comforting similarities that ease the transition and, just as importantly, the critical differences that can trip up a new user. Finally, we will compare the overarching philosophies of the two ecosystems, contrasting the walled-garden, proprietary model with the open-source, community-driven approach. This head-to-head analysis will provide the necessary context for MATLAB users to understand not just how to switch, but why the switch is so compelling.
The Performance Showdown: Why Speed Matters
Julia was designed with technical and scientific users in mind. To solve business problems and support decision-making, data professionals often have to deal with large datasets and complex techniques, including machine learning algorithms and artificial neural networks. These activities not only require important computing resources, they also require a programming language that can run code on a computer very quickly, so data professionals don’t have to wait for long to get their results. Traditionally, programming languages with high performance have been trickier to use, and vice versa. For example, popular high-level languages like MATLAB and Python sacrifice speed for being flexible and highly readable, while languages like C, C++, and Fortran are known to be very fast, but they have a very complex syntax. This means that developers need more time and often more programming experience to write code with these kinds of languages. This trade-off is the very definition of the “two-language problem” discussed in Part 1. Julia enters the scene to eliminate this compromise, offering both high-level, readable syntax and low-level, high-performance execution.
Interpreted vs. Compiled: The Real Difference
The most significant distinction driving the performance gap is how the languages are executed. MATLAB is, for the most part, an interpreted language. When you run a MATLAB script, the interpreter reads each line, parses it, and executes the corresponding command. While modern versions of MATLAB include a Just-In-Time (JIT) compiler to speed up certain operations, particularly loops, its core design remains interpreted. This “read-as-you-go” approach is excellent for interactivity and ease of use, but it carries a persistent overhead. Each line must be re-analyzed, and the dynamic nature of variables makes optimization difficult. Julia, by contrast, is a JIT-compiled language from the ground up, built on the LLVM compiler infrastructure. The first time you call a function, Julia’s JIT compiler analyzes the types of the inputs, generates highly specialized and optimized machine code for that specific set of types, and then executes that code. This compiled code is cached. Every subsequent time you call that function with the same input types, Julia skips the compilation and runs the pre-compiled, blazing-fast machine code directly. This means that while the first run of a Julia function might feel slow (the “time-to-first-plot” problem), subsequent runs are executed at speeds comparable to C or Fortran.
Benchmarking: A Deeper Look at Loops and Linear Algebra
To illustrate Julia’s performance, benchmark tests are often run. For example, consider a simple, non-vectorized for loop that performs a mathematical operation, such as creating a matrix from a given vector. In MATLAB, non-vectorized for loops are notoriously slow. The interpreter overhead on each iteration adds up significantly. MATLAB users are heavily trained to “vectorize” their code, replacing loops with matrix and vector operations (e.g., using bsxfun or implicit expansion). This vectorized code is fast because it calls highly optimized, pre-compiled C or Fortran libraries under the hood. However, this forces the programmer to think in a specific, often less-intuitive, vectorized way. Julia does not have this limitation. Because Julia’s JIT compiler optimizes loops so effectively, a handwritten for loop in Julia is often just as fast, or even faster, than a “vectorized” equivalent. This is liberating. It means MATLAB programmers can write code in the most natural, readable way (which is often a loop) and trust the compiler to make it fast. When comparing the performance of the two languages on these “scalar” loop-heavy tasks, Julia is significantly faster than MATLAB. For pure linear algebra, like a large matrix multiplication, the performance is more comparable, as both languages ultimately rely on optimized BLAS libraries.
Syntax: The Comfort of Familiarity
If you are considering moving from MATLAB to Julia, here’s the good news: the two languages are pretty similar, syntactically speaking. This fact comes with no surprise; in the end, MATLAB has been one of the main sources of inspiration for the founders of Julia. This familiarity is a massive advantage. For instance, both languages use for i = 1:N or for i in 1:N to start a loop, and both use the end keyword to terminate blocks (like for, if, and function blocks). This shared structure makes reading simple Julia code almost trivial for a MATLAB user. Function calls look identical: my_function(x, y). Basic arithmetic and logical operators are the same. This shallow learning curve allows MATLAB users to become productive in Julia very quickly. They can start by translating their existing .m files almost line-by-line, focusing on the logic rather than wrestling with semicolons, curly braces, or complex object-oriented syntax. This shared heritage is intentional, designed to attract the massive existing user base from the scientific computing world.
Indexing: The Crucial One-Based Similarity
Perhaps the most important and comforting similarity for MATLAB users is that Julia, like MATLAB and Fortran, is a one-indexed language. This means the first element of any array is accessed with the index 1, as in my_vector[1]. Most other popular “data science” languages, such as Python, R, C, and Java, are zero-indexed, where the first element is at index 0. This difference is a major source of cognitive dissonance and “off-by-one” errors for programmers switching between languages. By adopting one-based indexing, Julia signals its clear alignment with the conventions of scientific and mathematical computing. MATLAB users do not need to retrain their brains to think in terms of zero-based offsets. They can directly apply their existing mental models for slicing, dicing, and iterating over matrices and vectors. This single design choice makes the transition from MATLAB to Julia one of the smoothest possible, as array manipulation is at the heart of nearly all numerical computing tasks.
Key Syntactical Divergences
Despite the similarities, it’s worth noting that Julia is not a MATLAB clone. There are major syntactic and functional differences that a new user must learn. One of the first differences is in function definitions. In MATLAB, a function is typically defined in its own .m file, and the file name must match the function name. Julia is not file-based; you can define many functions in one file. A MATLAB function function out = f(x) becomes function f(x) in Julia, and the return keyword is often used explicitly (return x^2). Another critical difference is the explicit end-of-line character. MATLAB is more flexible, allowing you to use a semicolon ; to suppress output or just a newline. In Julia, newlines are generally used to separate statements, and semicolons can be used to chain commands on a single line. A more subtle but powerful difference is that variable names in Julia can use a vast range of Unicode characters. This allows you-to use actual Greek letters, like \alpha or \beta, as variable names (\alpha = 0.5), making the code look much closer to the mathematical formulas it represents.
The Type System: A Glimpse of Julia’s Power
The biggest philosophical difference under the hood is the type system. MATLAB is dynamically typed, and almost everything is treated as a matrix. A scalar is a $1 \times 1$ matrix, a vector is an $N \times 1$ or $1 \times N$ matrix. This simplicity is a virtue, but it can also be a source of ambiguity and performance issues. Julia is also dynamically typed, which means you don’t have to declare variable types. You can write x = 10 and Julia will infer that x is an integer. However, Julia also has a rich, user-accessible type system. You can optionally annotate variables and function arguments with types, like function my_func(x::Float64). This is not primarily for performance, as the JIT compiler figures this out anyway. Instead, this type system is the key to Julia’s most powerful feature: multiple dispatch. This feature, which we will explore in depth in a later part, allows you to define multiple versions of the same function (e.g., process(x::Integer) and process(x::String)) and Julia will automatically call the correct one based on the types of the arguments provided at runtime. This is a far more flexible and powerful paradigm than MATLAB’s simple function overloading.
Open Source vs. Proprietary: The Ecosystem Impact
Finally, the difference in their business models has profound implications for the user experience. MATLAB is a proprietary, closed-source product. Its ecosystem consists of built-in functions and polished, expensive, first-party toolboxes. The quality is generally high, the documentation is unified, and there is a single company to call for support. However, you are limited to what the company provides, and the cost is a major factor. Julia is a free, open-source language with a liberal license. Its ecosystem consists of over 8,000 (and growing) registered packages developed by the community. This means you can find a package for almost anything, and if you can’t, you can build it yourself. You can read the source code of any package, or even of Julia’s standard library, to understand exactly how it works. This fosters a vibrant, collaborative community. The downside is that the quality and documentation of third-party packages can vary, and finding the “right” package can sometimes require more research than just buying the “official” MATLAB toolbox.
Thinking in Julia
Transitioning from MATLAB to Julia involves more than just learning new syntax; it requires a subtle shift in how you think about your data and operations. While Part 2 highlighted the high-level similarities, this part delves into the practical, foundational elements you use every day. We will start with the building blocks of any program: variables and data types. We will see how Julia’s rich type system contrasts with MATLAB’s “everything is a matrix” philosophy. Next, we will tackle the heart of MATLAB—the matrix—and compare it to Julia’s more nuanced and powerful array system. This will lead us to one of the most critical new concepts for MATLAB users: broadcasting. We will then expand our toolkit by mapping MATLAB’s familiar data structures, like cell arrays and structs, to their Julia counterparts, such as Tuples and Dictionaries. Finally, we will examine how to define and use functions, which will set the stage for understanding Julia’s single most important feature, multiple dispatch, in a future part. This chapter is your hands-on guide to translating your core MATLAB skills.
Variables and Data Types: From Matrices to Primitives
In MATLAB, the fundamental data type is the array. When you write x = 5, you are not creating a simple integer. You are creating a $1 \times 1$ matrix of type double (a 64-bit floating-point number). If you write y = ‘hello’, you are creating a $1 \times 5$ character array. This design simplifies matrix math but can be inefficient for other tasks. For example, storing a simple integer as a full 64-bit float wastes memory and can lead to subtle floating-point inaccuracies. Julia, on the other hand, has a rich system of primitive types. When you write x = 5, Julia infers this as an Int64 (a 64-bit integer). If you write y = 5.0, it becomes a Float64 (the same as MATLAB’s double). This distinction is crucial. Operations on integers are exact and often faster. Julia also has a true String type (using double quotes: z = “hello”) that is far more flexible and efficient for text manipulation than MATLAB’s character arrays. It also has a Char type (using single quotes: c = ‘a’) for single characters. This granularity gives you, the programmer, more control and allows the JIT compiler to generate much more efficient, specialized machine code.
The Matrix Laboratory vs. Julia’s Arrays
MATLAB’s name, “MATrix LABoratory,” reveals its core design. Vectors, matrices, and N-dimensional arrays are all just different forms of the array type. A vector is just a matrix with one dimension of size 1. This can lead to ambiguity: is a $1 \times 5$ array the same as a $5 \times 1$ array? In many contexts, MATLAB treats them interchangeably, which can be convenient but also a source of subtle bugs. Julia makes a clear distinction. A 1D-array, or Vector, is fundamentally different from a 2D-array, or Matrix. A Vector created with v = [1, 2, 3] has a size of (3,), while a $3 \times 1$ Matrix created with m = [1; 2; 3] has a size of (3, 1). These two objects will behave differently in operations like matrix multiplication. This explicitness may seem like extra work at first, but it eliminates ambiguity and makes your code more robust. Julia’s arrays are also more general; you can have an array of anything, such as Array{String, 2} (a 2D array of strings), which is much cleaner than MATLAB’s cell arrays.
Broadcasting: The Dot Syntax
This is one of the most important syntactic and conceptual differences. In MATLAB, you are trained to “vectorize” your code. If you have two vectors, a and b, and you want to multiply them element-by-element, you use the .* operator: c = a .* b. If you want to apply a function, like sin, to every element, you just write y = sin(x), and MATLAB automatically “vectorizes” the sin function for you. This is implicit. Julia takes a different, more explicit approach. The regular operators +, *, etc., are reserved for their linear algebra definitions. a * b will try to perform a dot product or matrix multiplication. To perform an element-wise operation, you must explicitly broadcast the operation by prepending it with a dot. So, element-wise multiplication is c = a .* b, just like in MATLAB. But to apply the sin function, you must also broadcast it: y = sin.(x). This dot syntax is universal. Any function in Julia, including your own, can be broadcast over an array by adding a dot. my_function.(my_array). This is incredibly powerful. It’s an explicit signal to the compiler and to the reader that you are performing an element-wise operation, and it’s more general than MATLAB’s implicit vectorization, which only works for functions that were specifically designed to be vectorized.
From Cell Arrays to Tuples and Dictionaries
MATLAB programmers use cell arrays to store collections of disparate data types. For example, C = {1, ‘hello’, [1 2 3]}. This is a powerful “catch-all” container. The Julia equivalent of a heterogeneous array is A = [1, “hello”, [1, 2, 3]], which would be of type Array{Any, 1}. While this is possible, Julia provides better, more efficient tools. If you have a fixed-size collection of heterogeneous items, you should use a Tuple. Tuples are created with parentheses: t = (1, “hello”, [1, 2, 3]). Tuples are immutable, meaning they cannot be changed after creation. This immutability allows the compiler to optimize them heavily, making them much more performant than MATLAB’s cell arrays for tasks like returning multiple values from a function. For key-value mapping, MATLAB users often use containers.Map or, more commonly, structs. Julia’s primary tool for this is the Dict (Dictionary). You can create one with my_dict = Dict(“name” => “Alice”, “age” => 30). These are highly optimized hash maps and are central to many programming tasks.
Structs vs. Structs: A Deeper Dive
In MATLAB, a struct is a simple data container with named fields, like s.name = ‘Alice’ and s.age = 30. It’s a very flexible, dictionary-like object. Julia also has structs, but they serve a more profound purpose. A Julia struct is a way to define your own composite type. You define it with a block: struct Person; name::String; age::Int; end. This creates a new type, Person, in your program. You can then create instances of it: p1 = Person(“Alice”, 30). By default, Julia structs are immutable, which, like tuples, allows for significant compiler optimizations. If you need to modify the fields, you can declare it as a mutable struct. This ability to create new, first-class types is fundamental to programming in Julia. It allows you to build clean, modular, and performant abstractions. Instead of passing around 8 different arrays representing the state of a simulation, you can pass a single SimulationState object that you defined yourself. This is a key feature that bridges the gap between high-level scripting and low-level systems programming.
Defining Functions in MATLAB and Julia
In MATLAB, functions are typically defined in their own .m files, and the file name must match the function name. For example, function out = f(x) would be saved in f.m. MATLAB also supports anonymous functions, like f = @(x) x^2. Julia’s approach is more flexible. Functions are first-class citizens, meaning they can be treated like any other variable: assigned, passed as arguments, or returned from other functions. You can define functions using the function keyword: function f(x); return x^2; end. You can also use a compact, one-line syntax: f(x) = x^2. This is very common and reads just like mathematical notation. Anonymous functions are also more powerful and use a “stabby arrow” syntax: x -> x^2. For example, to square all elements in an array, you could use the map function with an anonymous function: map(x -> x^2, [1, 2, 3]). This flexibility in defining and using functions is essential for writing advanced, composable code.
A Glimpse of Multiple Dispatch
We will dedicate a full section to this later, but it’s important to introduce it here as it relates to functions. In MATLAB, if you want a function to behave differently based on its input, you typically use an if statement inside the function to check the type or properties of the input. For example, if ischar(x) do one thing, elseif isnumeric(x) do another. This can become very messy. Julia solves this with multiple dispatch. You simply define the same function multiple times with different type annotations for the arguments. For example: process(x::Int) = “Got an integer.” and process(x::String) = “Got a string.”. When you later call process(10), Julia’s compiler sees the type of the input is Int and automatically calls the first version. When you call process(“hello”), it calls the second version. This is not just for simple types; it works for your own custom structs as well. This allows you to “extend” existing functions to work with your new data types. You could define a + operation for your Person struct, for example. This is arguably Julia’s most powerful feature and the key to its combination of performance and flexibility.
Rebuilding Your Toolkit
For any MATLAB programmer, a significant portion of the language’s value lies not in the core syntax, but in the vast, professionally-maintained ecosystem of toolboxes. When you need to perform statistical analysis, optimize a function, process a signal, or plot your results, you simply call functions from the relevant toolbox. Moving to Julia means rebuilding this toolkit, but with open-source packages. The good news is that the Julia ecosystem is vibrant, mature, and rapidly growing, offering powerful, community-driven alternatives for nearly every MATLAB toolbox. This part of our series is a practical guide to this new ecosystem. We will start by exploring Julia’s built-in package manager, which replaces MATLAB’s Add-On Explorer. We will then map core functionalities: how to move from MATLAB’s table type to the indispensable DataFrames.jl package, how to replace the Statistics Toolbox with Julia’s StatsBase.jl and Distributions.jl, and how to create rich visualizations using Plots.jl. We will even look at the growing machine learning landscape and, most importantly, show you how to call your existing MATLAB code from Julia, ensuring your transition can be gradual and productive.
Package Management: Toolboxes vs. Pkg.jl
In MATLAB, acquiring new functionality means opening the Add-On Explorer, searching for a toolbox, and installing it. If it’s a first-party toolbox, this often requires purchasing an additional license. The process is centralized and controlled by the software vendor. Julia’s approach is fundamentally different and, for many, liberating. Julia comes with a sophisticated, built-in package manager named Pkg. This manager is accessible directly from the Julia REPL (its interactive command line). To enter the package manager, you simply press the ] key. The prompt changes to a “pkg>” prompt. From here, you can add any registered open-source package for free. For example, to add the data frames package, you simply type add DataFrames. To update all your packages, you type update. Pkg.jl handles complex dependencies, manages different versions of packages for different projects (using Project.toml and Manifest.toml files, similar to Python’s requirements.txt), and makes the process of building a reproducible scientific environment trivial. This is a massive improvement over manually managing MATLAB paths or add-ons.
Data Wrangling: From MATLAB Tables to DataFrames.jl
In modern MATLAB, data analysis often begins by loading data into a table. This data type provides a convenient, spreadsheet-like container with named columns that can hold different data types. It’s a powerful and necessary addition to MATLAB’s matrix-centric world. The undisputed equivalent in Julia is the DataFrames.jl package. This package provides the DataFrame type, which is conceptually identical to a MATLAB table or an R/Python DataFrame. You can load data from a CSV file directly into a DataFrame, select columns by name using symbols (e.g., df[!, :MyColumn]), filter rows based on conditions, and perform complex group-by-and-summarize operations. The syntax for DataFrames.jl is heavily inspired by other data-wrangling toolkits and is extremely powerful. Combined with other packages like CSV.jl for reading files and the “query”-like syntax provided by other packages, it provides a complete and high-performance environment for data manipulation that meets or exceeds the capabilities of MATLAB’s tables. For MATLAB users, learning to use DataFrames.jl is the first and most important step in replicating their data analysis workflow.
Plotting: Goodbye plot(), Hello Plots.jl
MATLAB’s integrated plotting functionality is one of its greatest strengths. The plot(), scatter(), histogram(), and imagesc() functions are simple, effective, and produce publication-quality graphics with minimal effort. The results appear in a dedicated, interactive figure window. Julia’s plotting ecosystem is more fragmented but also more flexible. There is no single “built-in” plotting library. Instead, there are many competing packages. However, a “meta-package” called Plots.jl has emerged as the most popular starting point. The genius of Plots.jl is that it provides a single, consistent API (e.g., plot(), scatter()) that can “talk” to many different plotting “backends.” You can write your plotting code once using the Plots.jl syntax. If you want a fast, lightweight, static plot, you can tell Plots.jl to use the GR backend. If you want a fully interactive, web-based plot that you can zoom and pan, you can tell it to use the Plotly or PlotlyJS backend, all without changing your plotting commands. This gives you incredible flexibility. While it can take a moment to generate the first plot due to JIT compilation, subsequent plots are fast, and the quality and interactivity options are far beyond what MATLAB’s built-in system offers.
Statistical Analysis: Replacing the Statistics Toolbox
The Statistics and Machine Learning Toolbox is a workhorse for many MATLAB users. It provides functions for descriptive statistics (mean, std), probability distributions (normpdf, normcdf), hypothesis testing (ttest2), and generalized linear models (fitglm). The Julia ecosystem provides open-source alternatives for all of these, split across several specialized, composable packages. For basic descriptive statistics, the Statistics standard library (included with Julia) and the StatsBase.jl package provide what you need, including functions for mean, median, std, cor, and robust statistics. For probability distributions, the Distributions.jl package is the standard. It provides a rich framework for creating and querying an extensive library of probability distributions. For linear models, the GLM.jl package (Generalized Linear Models) is the direct equivalent to MATLAB’s fitglm and R’s lm and glm functions. It uses a formula syntax that will be familiar to R users and is easy for MATLAB users to learn. The combination of these packages provides a comprehensive, powerful, and transparent foundation for statistical analysis in Julia.
Machine Learning: From Toolboxes to Flux.jl
MATLAB’s Statistics and Machine Learning Toolbox also provides a suite of tools for classic machine learning, such as SVMs, decision trees, and k-means clustering. For deep learning, the Deep Learning Toolbox provides an integrated environment for building and training neural networks. Julia’s machine learning ecosystem is one of its most exciting and rapidly developing areas. For classical machine learning, MLJ.jl (Machine Learning in Julia) provides a unified meta-framework, similar to Python’s scikit-learn, for comparing, tuning, and deploying a wide varietyf of models. The real power, however, lies in deep learning. Packages like Flux.jl are written entirely in Julia. This is a stark contrast to MATLAB or Python, which rely on monolithic, compiled C++ backends (like TensorFlow). Because Flux.jl is just Julia code, it is fully “hackable.” You can see every line of the implementation, and it composes perfectly with the rest of the Julia ecosystem, such as the differential equations solvers. This leads to a field known as “scientific machine learning,” where neural networks can be embedded inside physical models, which is a cutting-edge capability that is uniquely suited to Julia’s design.
Optimization: The JuMP.jl Revolution
For MATLAB users in engineering and finance, the Optimization Toolbox is indispensable. It provides solvers for linear programming (LP), mixed-integer programming (MIP), quadratic programming (QP), and general nonlinear optimization. This is one area where Julia does not just compete, but is widely considered to have a superior solution. The JuMP.jl package is a domain-specific modeling language for mathematical optimization embedded entirely within Julia. It provides a clean, algebraic syntax to describe your optimization problem. What makes JuMP.jl revolutionary is that it is solver-agnostic. You can write your problem definition once, using JuMP.js syntax, and then plug in any of a dozen different solvers—commercial (like Gurobi or CPLEX) or open-source (like HiGHS or Ipopt)—to actually solve it. This allows you to easily benchmark different solvers or switch providers without rewriting any of your model logic. JuMP.jl has been so successful that it has been adopted by major institutions and is a primary driver for Julia’s adoption in operations research and energy systems modeling.
Interoperability: You Don’t Have to Quit Cold Turkey
Even with this powerful ecosystem, MATLAB users may have legacy codebases or specific, niche toolboxes that have no Julia equivalent. The Julia community understands this, and has provided excellent interoperability tools. The first is MAT.jl. This package allows you to read and write MATLAB’s .mat files directly from Julia, making data exchange seamless. You can save your results in Julia and load them in MATLAB, or vice-versa. The second, and more powerful, tool is the MATLAB.jl package. This package allows you to start a MATLAB session in the background and call MATLAB functions directly from your Julia code. You can send Julia variables to the MATLAB workspace, execute a MATLAB command (like a function from a specific toolbox), and retrieve the results back into Julia. While this carries a performance penalty (as it’s running MATLAB under the hood), it is an invaluable tool for a gradual transition. It allows you to port your application piece by piece, replacing the bottlenecks with fast Julia code while keeping your legacy, hard-to-port MATLAB functions running.
Beyond Basic Translation
By this point, you have learned how to replicate your core MATLAB workflow in Julia. You understand the basic syntax, the new data structures, the package manager, and the key libraries for data science and plotting. If you stopped here, you would have a “faster MATLAB” that is free and open-source. However, to truly unlock the power of the language, you must move beyond simple translation. Julia is not just a faster clone; it is a language with a fundamentally different and more powerful design. This part of our series is dedicated to the advanced features that have no direct equivalent in MATLAB. We will explore metaprogramming and macros, the @ symbol you may have seen, and how it allows you to manipulate code itself. We will dive into performance engineering, learning why some Julia code can be slow and how to use tools like type annotations and the profiler to make it fast. We will cover Julia’s built-in and easy-to-use parallel computing features, which replace the costly Parallel Computing Toolbox. Finally, we will revisit structs and see how they are the key to Julia’s most powerful feature: multiple dispatch.
Understanding Metaprogramming and Macros
MATLAB has very limited metaprogramming capabilities, primarily through functions like eval or feval, which allow you to execute code from a string. This practice is generally discouraged as it is slow, insecure, and hard to debug. Julia, on the other hand, was designed with a powerful metaprogramming system inspired by Lisp. In Julia, code itself can be represented as a data structure, specifically an Expression object. You can build these expression objects, manipulate them, and then tell Julia to evaluate them. This is what a macro does. Macros are functions that run at parse time (before your code is compiled) and whose inputs are Julia expressions and whose output is a new Julia expression. You recognize them by the @ symbol. A simple example is the @time macro. When you write @time my_function(x), the @time macro takes the expression my_function(x) as its input. It then generates and returns a new block of code that looks something like tic(); result = my_function(x); toc(); println(elapsed_time); result. Julia then compiles and runs this new code. This allows you.to write incredibly powerful, time-saving utilities.
Performance Engineering: Writing Performant Julia Code
One of the common complaints from new Julia users is that their code is slow, sometimes even slower than MATLAB. This is almost always due to a misunderstanding of how Julia’s JIT compiler works. The compiler generates fast code when it can infer the type of every variable at every step. If it cannot determine a variable’s type, it must generate slow, “generic” code that can handle any possibility. This is called “type instability.” The number one cause of type instability is using non-constant global variables. MATLAB programmers are used to defining variables in the base workspace and then modifying them from scripts or functions. In Julia, this is a performance killer. To write fast Julia code, you must wrap your logic in functions. Variables inside a function are local, and the compiler can easily track their types. You should also avoid creating containers that hold heterogeneous data, like [1, “hello”, 3.0], as these become Array{Any}, forcing the compiler to be generic. By writing “type-stable” functions (functions where the type of the output can be inferred from the types of the inputs), you enable the JIT compiler to do its job and produce machine code that is as fast as C.
Using the Profiler to Find Bottlenecks
When your Julia code is still slow even after you have wrapped it in functions, the next step is to profile it. MATLAB has a built-in profiler that is very easy to use. Julia also has a built-in statistical profiler. You can run your function under the profiler, and it will collect “snapshots” of the call stack thousands of times per second. This data can then be analyzed to show you exactly which lines of code are taking the most time or, more importantly, which lines are allocating the most memory. Excessive memory allocation (creating many temporary arrays) is a common performance bottleneck. The profiler will highlight these allocations in its output, allowing you to pinpoint the exact location in your code that needs to be optimized. For example, you might find that you are accidentally creating a new array in the middle of a hot loop. You can then rewrite that part of the code to be “non-allocating,” perhaps by pre-allocating the array outside the loop and updating it “in-place” (which we will discuss next). Julia’s profiling tools are powerful and essential for writing high-performance code.
The Importance of In-Place Operations
In MATLAB, most functions are “out-of-place.” When you write y = sin(x), MATLAB creates a new array y to store the result. This is generally fine, but if you are doing this inside a large loop, you are allocating memory on every single iteration, which is slow. Julia gives you the choice. It also has out-of-place functions, like y = sin(x). However, for many operations, it also provides “in-place” or “mutating” versions. By convention, these functions have a ! at the end of their name. For example, if A and B are matrices, C = A * B is an out-of-place multiplication that creates a new matrix C. But the mul! function, mul!(C, A, B), performs the multiplication and stores the result in the existing matrix C, overwriting its contents. This allocates no new memory. Similarly, sort(v) returns a new sorted vector, while sort!(v) sorts the vector v in-place. MATLAB programmers must learn to spot opportunities to use these in-place functions (or their broadcasted equivalents, like y .= sin.(x)) to write efficient, low-allocation code, especially inside loops.
Parallel Computing Made Easy
In MATLAB, to run code in parallel, you must purchase the Parallel Computing Toolbox. This gives you access to parfor, a parallel for loop, and other related functionalities. It is powerful but costly and can be complex to configure. Julia, in contrast, was designed for parallel computing from the beginning, and its core capabilities are built directly into the language, for free. Julia provides two main models of parallelism. The first is multithreading, which is useful for “shared-memory” parallelism on your local machine (using all the cores in your CPU). You can launch Julia with multiple threads, and then use the Threads.@threads macro to instantly parallelize a for loop. It is as simple as writing Threads.@threads for i in 1:N … end. The second model is multiprocessing (or distributed computing), which is for “distributed-memory” parallelism, either on one machine or across a cluster. You can add worker processes and then use functions like pmap (parallel map) or macros like @distributed to spread the work across all available CPUs. This built-in, easy-to-use parallelism is a massive advantage for scientific computing.
Creating Your Own Types with Structs
We introduced structs in Part 3 as a way to define your own data types. In MATLAB, you might use a struct as a simple container (s.name = “Alice”), or you might use classdef to dip into object-oriented programming. Julia’s structs are simpler than classdef but far more central to the language’s design. When you define struct Person; name::String; age::Int; end, you are creating a new, concrete type that the Julia compiler understands as well as it understands Int or Float64. You can create arrays of this type: my_team = Vector{Person}(undef, 10). This is a packed array in memory, which is incredibly efficient—far more so than a MATLAB cell array of structs. This ability to create lightweight, efficient, composite types is a cornerstone of Julia. It allows you to organize your code in a way that is both readable (grouping related data together) and highly performant (allowing the compiler to optimize storage and access). This concept is the key that unlocks Julia’s ultimate feature.
Multiple Dispatch: Julia’s Secret Weapon
This is the most important concept in Julia, and it has no real parallel in MATLAB. As mentioned in Part 3, multiple dispatch is the ability to define many different methods for a single function, based on the types of the arguments. Let’s combine this with our custom struct. Imagine we have our Person struct and we want to define what it means to “greet” them. We can write a function: greet(p::Person) = println(“Hello, $(p.name)!”). Now, let’s say we also have struct Dog; name::String; end. We can define another method for the same function: greet(d::Dog) = println(“Who’s a good boy, $(d.name)?”). Now, if we have an array of mixed pets, pets = [Person(“Alice”, 30), Dog(“Fido”)], we can just write for pet in pets; greet(pet); end. Julia will, at runtime, look at the type of the pet variable in each iteration and automatically call the correct method of greet. This is not an if statement. It is a fundamental property of the language. This paradigm is incredibly powerful. The Plots.jl package, for example, doesn’t need to know about your Person struct. You can just define a new method for Plots.plot(p::Person) and tell it how to visualize your object. This makes Julia code extraordinarily extensible and composable.
Making the Leap
In the previous five parts, we have journeyed from a high-level comparison of MATLAB and Julia to a deep, technical dive into Julia’s most advanced features. You have learned how to translate your MATLAB syntax, replicate the functionality of your favorite toolboxes, and leverage powerful new concepts like multiple dispatch and metaprogramming. You now have the “how-to” knowledge. This final part of our series is about the “what’s next.” Knowing a language is one thing; successfully integrating it into your professional life is another. This part is a practical guide to managing that transition. We will cover a strategic, step-by-step approach to porting your first project, making the case for Julia to your colleagues and managers, and engaging with the open-source community to get help and contribute. We will also analyze the current job market for Julia skills and discuss why learning this language now could be a powerful strategic move for your long-term career. Finally, we will look to the future, exploring the trends that are shaping Julia’s destiny in the world of scientific computing.
Step 1: Learn and Practice the Basics (A Recap)
The best way to learn a new programming language is by writing code. Before you attempt to port a massive, mission-critical MATLAB application, you must solidify the fundamentals. There are many resources out there for getting started with Julia, including the excellent official documentation, numerous tutorials, and online courses. We highly recommend you check out any introductory course, as many are developed for newcomers to Julia and, more broadly, beginners in data science. A good course will start with the very fundamentals of the language and move progressively towards more detailed elements. By the end, you’ll be familiar with coding in Julia, understanding the basics, including data types and structures, the functions and packages, and how to use DataFrames to work with tabular data. You should also find and keep a handy cheat sheet nearby. This initial investment in structured learning will pay for itself many times over by preventing the frustration of debugging basic syntax and type-related errors.
Step 2: Porting Your First Project
A great way to consolidate your knowledge and get essential hands-on experience is by using the language to work on your favorite data projects. Find a small, self-contained MATLAB project that you know well. This could be a script that cleans a data file, a set of functions that run a simple simulation, or a program that generates a specific set of plots. Do not pick your most complex, time-sensitive, or poorly-understood project. The goal here is learning, not immediate production. As you port the code, you will encounter your first real-world challenges. You will be forced to find the Julia equivalents for MATLAB functions, you will struggle with type-instability and slow code (and then learn to fix it by putting it in a function), and you will learn to debug. This process is invaluable. Using Julia in your own projects will help you discover its full potential and begin the process of building a portfolio of Julia code, which is essential for your professional development.
Step 3: Integrating Julia in a Professional Environment
Once you are familiar with Julia, it’s time to leverage it in your daily activities. Julia’s combination of high performance and readability can make it a game-changer when working in a professional setting. The easiest way to start is by using Julia for “offline” tasks that don’t disrupt the main production pipeline. Perhaps you have a MATLAB process that takes four hours to run. Try rewriting it in Julia on the side. When you get it running in 10 minutes, you have a powerful success story. This is how you build a case. You can also use Julia’s interoperability. Use MAT.jl to read the .mat file outputs from your team’s MATLAB process, perform your super-fast analysis in Julia, and then write your results back to a .mat file for your colleagues to use. Or, use MATLAB.jl to call that one obscure toolbox function you need, while writing the 99% of your logic—the part that is a performance bottleneck—in pure Julia. This gradual, low-risk integration is the key to introducing a new technology in an established environment.
The Julia Community: Where to Get Help
In MATLAB, your primary source of help is the official, extensive documentation and the vendor’s technical support. In the open-source world, your primary source of help is the community. Julia’s community is one of its strongest assets. It is famously welcoming, active, and intelligent. The primary gathering places are the official Julia Discourse forum and a community chat platform. The Discourse forum is the best place for long-form, technical questions. You will often get detailed, insightful answers from the actual developers of the packages you are using. For quick questions or more informal chat, the live chat channels are excellent. Unlike many online forums, the Julia community is known for being patient with newcomers, especially those coming from other languages like MATLAB. Engaging with this community is a critical skill. You will learn to ask good, reproducible questions, and in time, you will be able to answer questions yourself, which is the best way to deepen your own knowledge.
Career Opportunities: The Julia Job Landscape
Now is a great time to learn Julia. As many industry observers claim, Julia could be your golden ticket to the future if you start learning it before it becomes the new normal in data science. More and more companies worldwide are adopting Julia for their data projects, meaning that they will do whatever they can to attract the (still) few experienced Julia programmers. The key here is the “supply and demand” curve. The number of jobs asking for Julia is smaller than the number asking for Python or MATLAB, but the number of qualified Julia programmers is far, far smaller. This means that candidates who are proficient in Julia are extremely valuable. They command high salaries and often get to work on the most interesting, cutting-edge problems in fields like pharmaceutical modeling, climate science, robotics, and quantitative finance. Having “Julia” on your resume signals that you are a forward-thinking programmer who cares about performance and modern tools. It distinguishes you from the crowd and makes you a much more compelling candidate.
The Future of Julia and Scientific Computing
Julia is one of the rising stars of the data space. Over the ten years since its first release, the language has gained maturity and today presents exceptional features that few languages can exhibit. While MATLAB is still in good shape, no programming language is safe in the rapidly evolving data science landscape. What is popular today can become irrelevant tomorrow, surpassed by more modern and powerful tools. In recent years, Julia’s main downside was its youth. However, today it is one of the most exciting new languages in the data science industry. Its “killer app” is scientific machine learning (SciML), the ability to merge traditional differential equations (modeling the physical world) with modern neural networks (data-driven models). This capability is powering new discoveries in drug development, materials science, and climate modeling. Julia is at the epicenter of this new field. As more companies realize the competitive advantage this provides, the demand for Julia and the developers who know it will only continue to grow.
Final Conclusion
We have covered the legacy of MATLAB, the promise of Julia, the head-to-head comparisons, the technical details of the transition, and the professional implications of making the switch. So, is Julia worth learning, then? And is it possible to progress from MATLAB to Julia? The answer to both of these questions is a resounding yes. For a MATLAB programmer, Julia is the most logical “next step.” It respects your existing knowledge of one-based indexing and matrix math while offering a path out of the proprietary, closed-source world. It solves the two-language problem, giving you the simplicity of MATLAB with the speed of C. The transition requires learning new concepts, particularly the explicit broadcasting syntax and the paradigm of multiple dispatch. But the rewards are immense: a faster, more expressive, and more powerful way to write code, all within a vibrant, free, and open-source ecosystem. Learning Julia today is an investment in your own future, positioning you at the forefront of the next great wave in scientific and high-performance computing.