Julia for Statistics - a tutorial

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This tutorial was prepared as part of the COST action HiTEc for the Econometrics & Statistics (Ecosta) 2025 conference in Tokyo.

Aim

This tutorial introduces the Julia programming language and its use for statistical analysis.
The following topics will be covered:

  • introduction to the Julia programming language

  • the package manager and tooling

  • managing data in Julia

  • plots for statistics

  • statistical distributions

  • likelihood inference using numerical optimization

  • working with R and Python in Julia

  • probabilistic programming for statistical inference using Turing.jl

Instructor


Mattias Villani
Professor of Statistics
Stockholm University

Before the tutorial

You may want to install Julia and VS code before the tutorial, to follow along in my interactive demos and also experiment yourself. I will not assume that you have installed Julia however, and you can attend the tutorial just to listen. See the first lecture for information on how to install Julia and (optionally, but recommended, VS Code). If you want to have the same packages and versions that I use in the tutorial, do this after you have installed Julia:

  • Download the Project.toml and Manifest.toml files and place them in your directory of choice.
  • Start Julia by typing julia in the terminal.
  • In Julia, cd to your directory by typing cd("PathToYourDirectory").
  • In Julia, type ] and press Enter to enter the package manager. The prompt should change to something with pkg>.
  • activate the environment by typing activate . and Enter. (that is activate followed by space and a dot).
  • Instantiate the environment with all dependencies by typing instantiate and Enter. This will takes some time since many packages are installed and precompiled.
  • Press Backspace to exit the package manager.

Workshop plan, materials and schedule

Lecture 1 - The Julia programming language and tooling
Time: 14.00-15.00
Reading: Getting started with Julia
Live demo: Basic Julia

🍎 leg stretcher

Lecture 2 - Working with data in Julia
Time: 15.00-16.00
Reading: Read, managing and plotting data
Code: DataFrames.jl | Tidier.jl | Plots

☕ tea break

Lecture 3 - Statistics in Julia
Time: 16.30-17.30
Reading: Distributions and Optimization | Interop with R and Python
Code: Distributions | Optimization | Working with R and Python

🍓 leg stretcher

Lecture 4 - Probabilistic programming using Turing.jl
Time: 17.30-18.30
Reading: Probabilistic programming with Turing.jl

More material

Books, courses and podcasts etc about Julia
A collection of Julia links
Writing Julia packages
More on packages and the package manager