A course focused on making intelligent, explainable, and understandable decisions when using analytics and avoiding common pitfalls in data modeling.
| This course teaches students to build bulletproof decision analytics by systematically avoiding the analytical traps that lead others into quicksand. Following Charlie Munger's principle that "trying to be consistently not stupid" creates more value than chasing analytical brilliance, students develop AI-powered practical skills in interpretable data modeling through hands-on challenges and engaged lectures. |
Future Book Cover |
- GitHub: Version control and project management
- Cursor AI: AI-assisted coding and development
- Quarto: Dynamic document creation and reporting
- Python: Data analysis and visualization
- DAFT/DOT: Graph visualization and decision modeling
- Random Variables: Understanding uncertainty and variability in data
- DAGs (Directed Acyclic Graphs): Modeling causal relationships and dependencies
- Causal Inference: Moving beyond correlation to understand causation
- Decisions: Framing analytical problems as decision-making processes
- Probability Distributions: Modeling uncertainty and risk in business contexts
- Ergodicity Economics: Understanding time-based vs. ensemble-based thinking
- Data Visualization for Storytelling: Communicating insights through compelling visuals
- Models: Understanding interpretability from transparent to black-box models