Simulate with Monte Carlo

Overview

Ready to turn uncertainty into opportunity? This Short Course was created to help data analysts master Monte Carlo simulation techniques to quantify risk and forecast outcomes under uncertainty. By completing this course, you'll transform complex probabilistic scenarios into actionable insights. You'll build robust simulation models, conduct sensitivity analysis, and create probability distributions that inform critical business decisions with confidence. By the end of this course, you will be able to: Build Monte Carlo simulation models for project ROI analysis Evaluate sensitivity through tornado chart analysis Construct currency-exchange exposure simulations Determine optimal iteration counts for model convergence This course is unique because it combines theoretical foundations with hands-on Python implementation, providing both Excel-based and programmatic approaches to Monte Carlo modeling. To be successful in this project, you should have a background in basic statistics and Python programming.

Syllabus

Module 1: Simulation Models - Foundation

Learners will understand the theoretical foundations of simulation modeling and prepare to build Monte Carlo models for business applications.

Module 2: Monte Carlo Simulation - Core Application

Learners will build functional Monte Carlo simulation models using Excel and Python, executing 10,000+ iterations to generate probability distributions for project ROI analysis.

Module 3: Risk Analysis & Convergence - Integration

Learners will master sensitivity analysis through tornado charts and convergence testing to determine optimal iteration counts for reliable simulation results.

Module 4: Practical Applications - Assessment

Learners will integrate all Monte Carlo simulation skills through comprehensive practical applications and demonstrate mastery via course-level graded assessment covering all learning outcomes.