Decision Tree Analysis Course
Master decision tree analysis from the ground up — from structuring complex problems to calculating expected values, sensitivity ranges, and the value of information. This course gives you a rigorous, practical framework for making smarter decisions under uncertainty. Whether you work in finance, strategy, operations, or risk management, these skills translate directly to high-stakes professional decisions.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
You will learn how to build and solve decision trees for sequential problems involving uncertainty, risk, and competing alternatives. The course covers probability fundamentals, Bayesian updating, and rollback analysis using expected monetary value. You will conduct sensitivity analyses, run Monte Carlo simulations, and calculate the value of perfect and imperfect information. Utility theory is introduced so you can model risk preferences beyond simple monetary outcomes. Advanced topics include capital investment decisions, multi-attribute trees, and competitive game-theoretic extensions. You will also learn how to communicate your analysis clearly to executive and technical audiences alike.
Elevify advantages
Develop skills
- Decision Tree Construction: Build complete, well-structured decision trees from real problem statements.
- Rollback Analysis: Apply backward induction to identify optimal decisions across multi-stage trees.
- Sensitivity Testing: Conduct one-way, two-way, and Monte Carlo analyses to assess decision robustness.
- Value of Information: Calculate EVPI and EVSI to justify research investments before committing to a decision.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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