Lesson Plan

• Describe how data mining is used in security, healthcare and marketing contexts.
• Explain key techniques (classification, clustering, association rules, anomaly detection) and their relevance to each domain.
• Evaluate ethical and legal issues associated with data mining applications.
• Compare benefits and challenges across the three sectors.
• Apply a simple case study to illustrate the data‑mining workflow.

• Projector and screen
• Slide deck summarising applications
• Handout with comparison table
• Sample anonymised data set (e.g., transaction log)
• Laptop for each student or pair
• Whiteboard and markers

Begin with a quick poll: “Where have you seen data mining in everyday life?” Connect responses to prior lessons on databases. Explain that today’s success criteria are to identify applications in three sectors and discuss their ethical implications.

1. Do‑now (5’) – Students write down three examples of data mining they know; share briefly.
2. Mini‑lecture (10’) – Overview of the data‑mining process and core techniques.
3. Domain deep‑dive (15’) – In groups, each tackles one sector (security, healthcare, marketing) using the handout and sample data.
4. Whole‑class synthesis (10’) – Groups present key findings; teacher highlights benefits, challenges and techniques.
5. Ethical discussion (10’) – Guided debate on consent, privacy and legal compliance using the listed considerations.
6. Exit ticket (5’) – Write one real‑world implication of data mining for a sector of choice.

Recap the three application areas, the techniques used, and the main ethical concerns. Collect exit tickets to gauge understanding, and assign a short homework: research a recent news article on data mining and prepare a one‑minute summary for the next class.