REFERENCES SOURCES & FURTHER READING

Sources and further reading

This tutorial is an original synthesis. It does not reproduce the wording or code of the sources below.

Process model

• Wirth, R., & Hipp, J. (2000). CRISP-DM: Towards a Standard Process Model for Data Mining. Proceedings of the 4th International Conference on the Practical Application of Knowledge Discovery and Data Mining.
• IBM · What is CRISP-DM?

Python and machine-learning implementation

• pandas documentation
• scikit-learn User Guide
• scikit-learn pipelines and column transformers
• scikit-learn model evaluation
• Matplotlib documentation
• The Turing Way · Reproducible research

Related open educational projects

These projects informed the structure of the tutorial but their text and code are not copied here.

• Microsoft · Data Science for Beginners
• Stefan Jansen · Machine Learning for Trading
• DrivenData · Cookiecutter Data Science
• Arthur Turrell · Coding for Economists
• QuantRocket · Quantitative Finance Lectures

Open data starting points

These sources can support an optional real-data extension. Dataset licences vary, so check the terms for each individual dataset before redistributing raw files. The core tutorial keeps synthetic data so every example remains deterministic and offline-reproducible.

• scikit-learn toy datasets
• UCI Machine Learning Repository
• Our World in Data
• World Bank Open Data

Data note

All datasets are generated from fixed random seeds. They contain no confidential, personal, or employer data and should be treated only as teaching examples.

Figures

The process and explanatory diagrams are original tutorial graphics. They synthesise concepts from the cited process, evaluation and reproducibility sources rather than copying screenshots from third-party courses.