Julia
High-performance scientific computing language from MIT. Julia runs as fast as C while feeling as readable as Python — the language NASA, MIT and the Federal Reserve use for their most demanding numerical work.
Quick facts
What is Julia?
The language that solves the two-language problem
Before Julia, scientists faced a frustrating tradeoff — prototype in Python (readable but slow) then rewrite performance-critical parts in C or Fortran (fast but painful). Julia was created in 2012 at MIT to eliminate this two-language problem entirely.
Julia achieves near-C performance through just-in-time compilation using LLVM — the same compiler infrastructure that powers C, C++ and Rust. Code is compiled to native machine instructions the first time it runs, not interpreted like Python.
Julia's central design concept is multiple dispatch — functions are selected based on the types of all arguments, not just the first. This enables a level of code reuse and genericity that is impossible in conventional object-oriented languages.
Julia's standard library includes world-class support for linear algebra, statistics, random numbers, FFTs and differential equations — all without installing packages. The package ecosystem (Pkg) adds machine learning (Flux.jl), plotting (Plots.jl), and data frames (DataFrames.jl).
What you will learn
- 1Variables, types and type annotations
- 2Arithmetic and mathematical operations
- 3String interpolation with $variable
- 4Control flow — if, elseif, for, while
- 5Functions, multiple dispatch and methods
- 6Arrays, matrices and linear algebra
- 7Tuples, Dicts and Sets
- 8Comprehensions and broadcasting
- 9Working with the standard library
- 10Plotting and data visualisation basics
Taste of Julia
Your first Julia program
Julia's syntax is clean and readable — close to Python and mathematical notation. Notice arrays start at index 1, and the dot syntax broadcasts operations across entire arrays without loops.
Reference
Julia Syntax Reference
The most important Julia syntax at a glance. Bookmark this — you will use it constantly.
x = 42Assign a variable (dynamic typing)x::Int = 42Type annotation — x must be Intconst PI = 3.14159Declare a constant"Hello, $name!"String interpolation — embed variable"Sum: $(a + b)"String interpolation with expressionif x > 0; endIf block (semicolons or newlines)if x > 0 ... elseif ... else ... endFull if/elseif/else blockfor i in 1:10; endFor loop from 1 to 10 inclusivefor x in collection; endFor loop over any iterablewhile condition; endWhile loopfunction greet(name); endFunction definitionf(x) = x^2Single-line function definitionx -> x^2Anonymous function (lambda)[1, 2, 3]Create a 1D array (Vector)[1 2; 3 4]Create a 2x2 matrixarr[1]Index an array — Julia is 1-indexed!arr[end]Last element of an arraypush!(arr, value)Append to array (! means mutates)Dict("a" => 1)Create a dictionary[x^2 for x in 1:5]Array comprehensionf.(arr)Broadcast function over array (dot syntax)arr .+ 1Element-wise addition (dot broadcasting)typeof(x)Get the type of a variableprintln("text")Print with newline# commentSingle-line commentFAQ
Common questions about Julia
What is Julia used for?
Julia is designed for high-performance numerical and scientific computing. It is used in computational physics, biology, economics, machine learning research, climate modelling, genomics and quantitative finance. Major users include NASA (planetary simulations), MIT, the Federal Reserve Bank (economic modelling) and pharmaceutical companies for drug discovery simulations.
How fast is Julia compared to Python?
Julia is typically 10x to 100x faster than Python for numerical workloads. It compiles to native machine code using LLVM (the same compiler infrastructure as C and Rust), while Python is interpreted. In many benchmarks Julia approaches within 2x of C performance. For data processing loops that would be slow in Python, Julia runs them natively without needing Cython or C extensions.
Should I learn Python or Julia for data science?
Start with Python. It has a vastly larger ecosystem (pandas, scikit-learn, TensorFlow, PyTorch), more job opportunities and better library support for general data science tasks. Learn Julia if you need maximum numerical performance, work in scientific computing, or your Python code is too slow and you have exhausted NumPy and Cython optimisations.
What is multiple dispatch in Julia?
Multiple dispatch is Julia's core design concept. When you call a function, Julia chooses which specific implementation to run based on the types of ALL arguments — not just the first one as in object-oriented languages. This allows Julia to write highly generic, reusable code and is why Julia can be so fast — the compiler knows exactly which specialised method to call.
Why does Julia use 1-based indexing?
Julia arrays start at index 1, not 0 like Python, C or Java. This was a deliberate choice because Julia is designed for scientists and mathematicians, who naturally count from 1. Mathematical notation for matrices and vectors also starts at 1. While it takes adjustment for programmers coming from 0-indexed languages, it is more intuitive for the scientific audience Julia targets.
Can I run Julia in my browser?
Julia requires compilation and cannot run live in a browser. On CodeLearn Pro, every Julia example shows the exact console output next to the code. You can run Julia online at juliabox.com or install it free from julialang.org — Julia is completely free and open source.
Ready to learn Julia?
10 examples covering variables, arrays, functions, multiple dispatch and scientific computing. Each shows the exact output.
Start the Julia tutorial →The Story of Julia
Real history, real companies, and an honest look at where Juliashines and where it doesn't — not just a feature list.
Where it came from
Julia was created by a team at MIT — Jeff Bezanson, Stefan Karpinski, Viral Shah and Alan Edelman — and publicly released in 2012, aiming to solve what its creators called the 'two-language problem': scientists would prototype in an easy language like Python, then have to rewrite the performance-critical parts in C for speed. Julia was designed to be both easy to write and fast to run, without that rewrite step.
How it’s actually used today
The US Federal Reserve has used Julia for large-scale economic modelling, and it's popular in climate science and computational biology for exactly the reason it was created — it lets researchers write high-performance simulation code without dropping into C. Pfizer and other pharmaceutical companies have used it for pharmacological modelling.
Strengths & trade-offs
Julia can genuinely match C's raw performance for numerical computing while reading almost like Python, which is a rare combination. Its trade-off is a much smaller ecosystem and community than Python — fewer tutorials, fewer pre-built libraries for non-scientific tasks, and a smaller hiring market outside research-heavy fields.
What to learn next
Since Julia is squarely aimed at scientific and numerical computing, a background in Python for data science makes the jump easier, mainly for comparison — the concepts of arrays, vectorised operations and data frames carry over directly between the two.