📉 Data & Analytics — Map of Content
Data analytics is the science of transforming raw data into actionable business insights through statistical methods, modeling, and visualization.
Growing in importance at: Wharton · Booth · All programs
📊 Statistics & Probability
- Descriptive Statistics — Mean, median, variance, distributions
- Hypothesis Testing — p-values, confidence intervals, Type I/II errors
- Regression Analysis — OLS, multiple regression, interpretation
- Statistical Significance vs. Practical Significance — The difference matters
🧪 Experimentation
- A-B Testing — Experimental design in business
- Randomization and Causality — RCTs vs. observational studies
- Statistical Power — Avoiding underpowered experiments
- Bayesian vs. Frequentist — Two schools of statistical inference
🤖 Predictive Modeling
- Decision Trees — Classification and regression trees
- Logistic Regression — Binary outcome prediction
- Clustering — K-means, segmentation methods
- Monte Carlo Simulation — Modeling uncertainty in decisions
🤖 AI & Large Language Models in Business
- Large Language Models — What they are and business applications
- AI Strategy — How companies build defensibility and competitive moats with AI
- Generative AI Business Uses — Use cases by business function (Finance, Marketing, Operations, HR)
- AI Governance — Hallucinations, bias, and regulatory risk management
- Prompt Engineering — Essential communication skills for working with AI models
- RAG — Retrieval-Augmented Generation for enterprise knowledge management
📈 Business Intelligence
- KPIs and Metrics — Leading vs. lagging indicators
- Dashboard Design — Effective data visualization principles
- Cohort Analysis — Retention and lifecycle analytics
- Funnel Analysis — Conversion optimization
🔑 Key Concepts
| Concept | Business Application |
|---|---|
| Correlation ≠ Causation | Avoid false inferences |
| Simpson’s Paradox | Aggregated trends mislead |
| Survivorship Bias | Only seeing winners |
| Overfitting | Model works on training, fails on new data |
| Base Rate Neglect | Ignoring priors in judgment |
📚 Essential Books
- Naked Statistics — Charles Wheelan (intuitive stats)
- The Signal and the Noise — Nate Silver (forecasting)
- Thinking with Data — Max Shron
🏫 School Spotlights
- Wharton: STAT 613 — predictive analytics; MKTG 776 — marketing analytics
- Booth: BUSN 41201 — Big Data; Econometrics curriculum
- HBS: TECH 610 — competing in the age of AI; Analytics track
📚 Case Studies
- Amazon Hiring Algorithm Bias
- Capital One Data Strategy
- Google Bard vs ChatGPT
- Google Hiring Algorithm
- IBM Watson in Healthcare
- Moneyball Oakland As
- Netflix Recommendation Engine
- OpenAI and the LLM Race
- Palantir Government Contracts
- Target Pregnancy Prediction
- UPS ORION Routing
- Zillow iBuying Failure
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