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Every piece of practice material for this course — Jupyter notebooks, exercise files, and real-world datasets — is bundled directly in the course repository. You do not need to download anything from external sites or create accounts to access them. If you have cloned the repository to your machine, you already have everything locally.

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What is included

The course materials are organised into two categories:

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Jupyter Notebooks (.ipynb)

Each major topic comes with two notebooks:
  • Practice notebooks — contain empty cells with helper hints so you can write the code yourself as you work through the lesson
  • Reference notebooks — show the completed code with explanations, so you can check your work or review a concept later

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Datasets (.csv)

Real-world CSV datasets are used in the Pandas, NumPy, and data visualisation modules. These include datasets such as student performance records and obesity prediction data — the same kind of messy, realistic data you will encounter in AI and data science projects.
If you cloned the repository with Git, all notebooks and datasets are already on your machine. You can open them directly in VS Code without any additional downloads.

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Accessing the materials

Go to Resources Hub

View the complete index of all notebooks and datasets, with quick download and Colab links.

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Running notebooks

You can open and run any .ipynb file in three ways: In VS Code (recommended): Open the file from the Explorer panel. Click Select Kernel and choose your virtual environment’s Python interpreter. In Google Colab: Each notebook page includes a Colab link. Click it to open the notebook in your browser with no local setup required — ideal for quick reference or if you are on a machine without a local Python environment. Download: Each notebook page also includes a direct download link so you can save the file and open it anywhere.
If you are working through the course for the first time, start with the practice notebook and try to complete the exercises yourself before looking at the reference notebook. Active recall and hands-on coding are far more effective for retention than reading alone.

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Keeping your resources up to date

If new notebooks or datasets are added to the course, you can get them by pulling the latest changes from the repository:
Run this from your local copy of the course repository whenever you want to sync with the latest materials.

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Next steps

With your resources ready, continue setting up your Python environment so you can run all the notebooks locally.

Virtual Environments

Learn how to create and manage isolated Python environments for your projects.