Data Mining and KDD Foundations

Why this module belongs in the course

Knowledge discovery is a decision process, while data mining is one analytical step inside it. This module keeps the question, user and evidence visible before an algorithm enters the project.

Before you begin

Basic computer use and a willingness to inspect evidence. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1What Is Data Mining? A Practical KDD Process55 min · Beginner
2. Lesson 2Frame a Data Mining Problem Before Choosing a Model60 min · Beginner
3. Lesson 3Responsible Data Mining: Provenance, Privacy and Fair Use65 min · Beginner to intermediate
4. Lesson 4Project: Build a Data Discovery Brief That Can Be Reviewed90 min · Beginner

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• From Data Mining to Knowledge Discovery in Databases
• scikit-learn: choosing the right estimator
• scikit-learn model evaluation
• scikit-learn common pitfalls
• NIST Privacy Framework
• Hugging Face dataset cards
• OECD AI Principles
• NIST AI Risk Management Framework