Julia Study Notes

Anonymous Functions and Closures

Learn Julia's -> syntax for anonymous functions, do-block syntax, and how closures capture variables from their enclosing scope.

Arrays in Julia

Learn how Julia's built-in Array type stores, indexes, and mutates collections of data, and why column-major memory layout matters for performance.

Broadcasting in Julia

Learn Julia's dot-syntax broadcasting mechanism, how chained operations fuse into allocation-free loops, and how to make custom types broadcast correctly.

Building a Data Analysis Script in Julia

A walkthrough of building a real data analysis script in Julia using DataFrames.jl, CSV.jl, and Statistics.

Calling Python and C from Julia

How PyCall.jl/PythonCall.jl let Julia call Python libraries directly, and how Julia's built-in ccall lets you call C and Fortran functions with zero overhead.

Conditionals in Julia

Learn how Julia evaluates truthiness, structures if/elseif/else blocks, and uses short-circuit and ternary operators for concise control flow.

Custom Types and Structs

Learn how to define composite types with struct, control mutability and construction, use parametric fields for performance, and attach behavior via multiple d…

DataFrames.jl Basics

An introduction to tabular data manipulation in Julia with DataFrames.jl — creating, inspecting, filtering, grouping, and joining data tables.

Functions in Julia

Learn Julia's function syntax, multiple return values, keyword and optional arguments, and the mutating-function naming convention.

Installing Julia and the REPL

How to install Julia with juliaup and navigate the four modes of Julia's interactive REPL for fast, iterative development.

Julia and Jupyter Notebooks

How to run Julia in Jupyter via IJulia, and how Pluto.jl offers a reactive, reproducible alternative notebook experience built specifically for Julia.

Julia and Machine Learning (Flux.jl)

How Flux.jl brings differentiable programming to Julia, letting you build and train neural networks with plain Julia code and automatic differentiation.

Julia Best Practices

Guidelines for writing fast, idiomatic, and maintainable Julia code, from type stability to package structure.

Julia for Scientific Computing

How Julia's multiple dispatch, type system, and native array performance make it a first-class language for numerical and scientific computing.

Julia Interview Questions

Common interview questions and model answers covering Julia fundamentals, performance, and multiple dispatch.

Julia Operators and Expressions

Julia's arithmetic, comparison, and logical operators, the dot-broadcasting syntax, and how the language's expression-oriented design ties it all together.

Julia Quick Reference

A condensed cheat sheet of core Julia syntax: variables, control flow, functions, types, and common collection operations.

Julia Variables and Types

How Julia's dynamic variables work, how its type hierarchy of abstract and concrete types is organized, and why type stability drives performance.

Julia vs Python

A practical comparison of Julia and Python for numerical computing, covering performance, syntax, and ecosystem tradeoffs.

Loops in Julia

Master for and while loops, iteration over collections, break/continue, and performance-minded loop patterns in Julia.

Matrices and Linear Algebra

Explore Julia's Matrix type and the LinearAlgebra standard library, from basic construction through factorizations to structured-matrix performance.

Multiple Dispatch Explained

Understand how Julia selects a method based on the runtime types of all arguments, and why this is central to Julia's design and performance.

Parallel Computing in Julia

An overview of Julia's parallelism options — multithreading with Threads.@threads, multiprocessing with Distributed.jl, and how to avoid data races.

Performance Tips in Julia

Practical techniques for writing fast, type-stable Julia code, from avoiding global variables to benchmarking with BenchmarkTools.jl.