Modeling Risk and Realities Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a practical and comprehensive introduction to modeling risk and decision-making under uncertainty, designed for professionals seeking to enhance their analytical skills. Through hands-on exercises using Excel, learners will build optimization and simulation models applicable to real-world business challenges. The course spans approximately 6 hours of content, divided into four core modules and a final project, offering lifetime access and a certificate upon completion.
Module 1: Modeling Decisions in Low Uncertainty Settings
Estimated time: 1 hour
- Introduction to optimization models in deterministic environments
- Building algebraic models and translating them into spreadsheet models
- Utilizing Excel Solver to identify optimal decisions
- Introducing basic risk elements into models
Module 2: Risk and Reward: Modeling High Uncertainty Settings
Estimated time: 1 hour
- Understanding high-uncertainty scenarios and associated risks
- Incorporating probability distributions and correlations into models
- Conducting sensitivity analysis
- Exploring the efficient frontier
Module 3: Choosing Distributions that Fit Your Data
Estimated time: 2 hours
- Visualizing data to identify suitable probability distributions
- Differentiating between discrete and continuous distributions
- Performing hypothesis testing to assess goodness of fit
Module 4: Balancing Risk and Reward Using Simulation
Estimated time: 1 hour
- Implementing simulation techniques to model uncertainty
- Analyzing simulation outputs to inform decision-making
- Comparing alternative decisions based on simulation results
Module 5: Final Project
Estimated time: 1 hour
- Build an optimization model for a low-uncertainty business scenario using Excel Solver
- Incorporate risk through probability distributions and scenario analysis
- Apply simulation and sensitivity analysis to evaluate and compare decisions
Prerequisites
- Familiarity with basic Excel functions
- Basic understanding of statistics and probability
- Interest in quantitative decision-making
What You'll Be Able to Do After
- Build optimization models for low-uncertainty scenarios using Excel Solver
- Incorporate risk into models through probability distributions and scenario analysis
- Select appropriate probability distributions based on data characteristics
- Utilize simulation techniques to evaluate decisions under uncertainty
- Apply sensitivity analysis to understand the impact of variable changes on outcomes