These authors’ work helped us review our course and identify useful next steps in statistics and practical machine learning.
We consulted the two repositories below when reviewing the course and designing the new data-inspection, sampling, prevalence, regularisation and repeated-validation activities. Relevant chapters are linked as optional reading, and each new activity names its conceptual references.
Our original activities: CodeWithImpact’s explanations, fictional scenarios, synthetic data and calculation engine are original. No source code, notebooks, datasets or images from these two repositories were imported or adapted in this update.
REPOSITORIES CONSULTED
The authors and their work
Statistics companion
Practical Statistics for Data Scientists
Peter Bruce, Andrew Bruce and Peter Gedeck. Second edition, O’Reilly Media, 2020. The companion repository contains examples in R and Python.
How it informed the lessons: chapters 1–2 guided the topics for our original data-inspection and sampling activities; chapter 4 supports residual diagnostics, chapter 5 classification interpretation, and chapter 6 repeated validation. The kitchen scenarios, data, questions and implementations were written for Code With Impact.
The repository is marked as no longer maintained. Its README points to the third-edition repository; the second edition remains the source reviewed here.
Hands-On Machine Learning with Scikit-Learn and PyTorch
Aurélien Géron. First PyTorch edition, O’Reilly Media. The companion repository provides Jupyter notebooks for the book.
How it informed the lessons: chapter 2’s workflow and the project checklist informed the original brief, validation and project activities; chapter 3 supports classification evaluation, and chapter 4 sample-size and regularisation experiments. Our optional notebook uses independently generated data and original code.
Further-reading notes name the authors and the relevant chapters. If we adapt a component in a future update, we will credit it where it appears and record its source, version, license and changes. Giving credit also means respecting the terms for the particular code, text, dataset or image.
Our source register records the reviewed versions and reuse details.
Repository review and credits updated 26 October 2026.
