Resources

Textbooks

[1] Matthew North (2012). Data Mining for the Masses. Global Text Project / Textbook Equity (Creative Commons). Main text — Free PDF
[2] Shmueli, Bruce, Gedeck & Patel (2019). Data Mining for Business Analytics: Concepts, Techniques and Applications in Python. Wiley.
[3] James, Witten, Hastie & Tibshirani (2023). An Introduction to Statistical Learning (2nd ed.). Springer (free PDF from authors).

Download: orangedatamining.com/download — choose your OS (Windows, macOS, Linux). The standalone installer bundles Python; no separate installation needed.
Tutorials: orangedatamining.com/getting-started — official video tutorials and sample workflows.
Widget catalogue: orangedatamining.com/widget-catalog — reference for every widget used in labs.
Example workflows: orangedatamining.com/blog/ — blog posts with downloadable .ows workflow files.

Google Colab — run Python in your browser, no installation needed. Recommended if local setup fails.
VS Code — free editor for local Python development.
pandas documentation — official reference.
pandas cheatsheet (DataCamp) — quick reference card.

Lab datasets are loaded directly in Orange using the Datasets widget (internet connection required) or provided in class:

Recommended starting points for your group project:

Kaggle Datasets — large curated collection; filter by "CSV" format. Requires a free account.
dataminingbook.com — 20+ datasets from the Shmueli et al. textbook, Excel- and Python-ready.
UCI Machine Learning Repository — classic benchmark datasets for all major ML tasks.
World Bank Open Data — macroeconomic and development indicators by country.
Vietnam Statistics Office (GSO) — national statistics in Vietnamese and English.
OECD Data — international comparisons across education, economy, health.

A good project dataset has at least 200 rows, clearly named columns, and a concrete business question worth answering.

Official Orange YouTube channel — short tutorials for each technique covered in the course.
Orange documentation — full widget and workflow reference.
Orange sample workflows for each week's technique are distributed in class and posted to the LMS.

CRISP-DM reference guide: PDF from SPSS Inc. — the original CRISP-DM 1.0 specification; concise and practical.
Introduction to Statistical Learning: statlearning.com — free PDF from the authors. Rigorous conceptual foundation for the techniques taught in Weeks 9–13.