Introduction to Julia

Introduction to Julia course at Imperial College

3 × 2 hour classes

• Part 1: Getting started, Functions, Conditionals
• Part 2: Data Structures, Packages, Plotting
• Part 3: Types, Multiple Dispatch, Error handling

Tutors :

• Jay DesLauriers

Past Tutors :

• Yiannis Simillides
• Evripides Georgiades

Acknowledgements :

The course materials come from Julia Academy lessons, and are being adapted for teaching at the Graduate School.

On completion of this workshop you will be able to:

• Use notebooks to execute Julia scripts and install packages.
• Apply fundamental components of the Julia language including variables, loops, conditionals and functions.
• Create programs designed to solve simple problems.
• Interpret common errors and use these to help debug a program.
• Understand advanced concepts, such as Multiple Dispatch and custom data types.

We will also share materials exploring domain specific packages used for:

• Data Frames
• Basic Linear Algebra
• Factorisations and SVD
• Ordinary differential equations

Prerequisites

• No programming experience is required
• Feel free to install Julia using the instructions below

Useful (Julia) Links

• https://docs.julialang.org/en/v1/
• https://julialang.slack.com/
• https://discourse.julialang.org/c/usage/first-steps/8

Feedback

This is the fourth time this course is running, this time with a slightly new format, so please feel free to let us know if anything needs changing / any feedback / difficulties or if there is anything that you particularly enjoyed!

Preparing Your Environment

In this course we'll be using the latest and greatest Julia v1.x via a Jupyter Notebook. This section will help you get set up with Julia.

Codespaces

The fastest way to get started with the course material is with a GitHub Codespace. You will need to login with a GitHub account to do so. We recommend signing up for GitHub Student Benefits, if you have not already.

Click the green <> Code button above the file listing on this page, select the Codespaces tab and click Create codespace on main. This will start a virtual environment where we can work on notebooks together.

The Codespace takes about 5 minutes to fully start. When ready, it will inform you that there is a service running on port 1234, and offer to take you there. Click that pop-up and you'll be taken to the notebook environment.

Julia Elsewhere

If you are keen on using Julia more regularly, we have outlined various options below.

Julia Locally

If you prefer to use Julia locally, see the official installation instructions, which use an installation manage called Juliaup.

Once installed, enter the Julia REPL by typing julia on the command line. Then use the following commands to install Pluto Notebooks:

import Pkg; Pkg.add("Pluto")

import Pluto;
Pluto.run()

You'll be prompted with a link to the Pluto environment.

Jupyter

If you would like to return to the comfort of Jupyter in the future, you'll need to install IJulia so you can access Julia via Jupyter. With that you should be ready to go!

using Pkg; Pkg.add("IJulia")

Imperial College RCS

The Research Computing Service runs JupyterHub. If you have access to RCS, this can be a great way to run Julia easily, with some power behind it. Navigate to jupyter.rcs.imperial.ac.uk and select a resource to get started. **For this course, the smallest resource is sufficient (1 core / 8GB RAM).

An old version of Julia is provided, so it is best to install the latest version. RCS provides instructions here (be sure to change the version numbers to 1.x).