About Agilearn

Agilearn is a set of self-directed Python guides, originally developed to support a second-year data science module and published here as a free, open resource.

Who it's for

The guides assume you have seen Python before — you recognise variables, strings, numbers, and basic control flow — but you are still new to writing Python that a working data scientist or developer would call "clean". If that sounds like you, start at any guide whose topic you're curious about; they're designed to be self-contained.

How the guides are organised

Each guide is structured into four sections.

Learn is the step-by-step path. Each item is a short Jupyter notebook you read through in order, running cells as you go. Start here if the topic is new to you.

Recipes are task-focused: "how do I write a recursive function?", "how do I avoid a common regex mistake?". Reach for these when you have a concrete job to do.

Reference is for lookups — the thing you skim when you need to remember a method signature or a flag. Short, information-dense, and not something you'd read cover-to-cover.

Concepts is the smallest but often the most interesting section. It holds short essays on why things work the way they do — why strings are immutable, why logging exists, why functions are first-class objects.

Running the code

Every notebook page has an Open in Lab button that launches the notebook in a browser-side Python environment. No install, no signup. Details are on the Lab page.

If you prefer to run locally, you can download each notebook with the download button next to the Lab button and open it in Jupyter or any editor with notebook support.

Licence and contributions

Content is MIT-licensed. The source lives on GitHub and contributions, corrections, and suggestions are welcome — there's an Edit this page link at the top of every page.