Why this decision matters
Most business value hides in the tails. A fraud rate of 0.5% — easy to ignore with accuracy as your metric — can be a multi-million-dollar opportunity. The same techniques that find fraud find equipment about to fail, customers about to leave, content going viral. We pair anomaly detection with market basket analysis — finding which items co-occur — because both are about spotting patterns in transactional data.
By the end of this topic you'll be able to
Distinguish point anomalies, contextual anomalies, and collective anomalies; choose a detection approach (statistical, distance-based, density-based, or model-based) for a given business problem; tune the trade-off between false alarms and missed events; mine transaction baskets to find item associations.
Materials
Key concepts to know
- Three types of anomalies — point (one weird value), contextual (weird given context, e.g., AC running at 3am), collective (a sequence that's weird as a group).
- Statistical methods — z-score, IQR, modified z. Quick, interpretable, assume distributions.
- Distance / density methods — K-nearest-neighbors, isolation forest, local outlier factor. Don't assume a distribution.
- Streaming detection — when you can't wait for the whole dataset; alerts in near-real-time.
- Market basket analysis — support, confidence, lift. "People who bought X also bought Y."
- Cost asymmetry — a missed fraud is worth $10,000; a false alarm is worth $5. Optimize accordingly.
Slides & class notes
- Anomaly Detection — MBA Lecture DeckBusiness-school framing: when each technique earns its keep.
- Anomaly Detection — Class NotesSelf-contained explainer covering all the methods on the slides.
- Anomaly Detection — Cheat SheetOne-page reference: which method, when, with what trade-offs.
Hands-on worksheet
- Anomaly Detection — In-Class WorksheetWork through three mini-scenarios; pick a detection approach and defend it.
Python notebooks
- Healthcare Vitals — Anomaly DetectionFind irregular patient vitals in continuous monitoring data.
- Sports Performance — Anomaly DetectionSpot athletes whose performance is drifting outside their personal norm.
- Streaming Anomaly DetectionDetect anomalies in real-time on a stream of events (StreamFlix data).
- Market Basket Analysis — LabApriori & FP-Growth on transaction data. Find the cross-sell rules.
- Demo: Anomalies in Time SeriesBonus — detecting anomalies when the data has a time dimension.
Practice with games · Anomaly & market-basket games
Six anomaly-flavored games and one cross-topic escape room. Start with the MBA Student Guide for orientation, then try the detective games.
- Anomaly Detection & Market Basket — MBA Student GuideSelf-paced walkthrough of every method we cover.
- In-Class Activity — Anomaly Detection (landing)The integrated activity hub for this topic.
- Anomaly Investigator GamePlay detective — find the anomalies hidden in real-shape data.
- Anomaly MBA Game (v2)Updated drilling game on the methods and when to use which.
- Pattern DetectiveBuild the muscle of "what doesn't belong here?" across multiple data types.
- Analytics Escape RoomCross-topic review with anomaly-detection puzzles — great pre-exam practice.
Sample presentations (executive vs. technical)
Same analysis, two audiences. Compare how the framing changes.