Simulating and Modelling to Understand Change

Module I - Introduction & Random Variables Simulation

• Elements of a Simulation Model
• Generating Random Numbers ↪
• Inferential Statistics and Models
• Introduction to Simulation and Modelling
• Key Features and Components of a Model
• Model
• Modelling Methodology
• Random Variable Simulation ↪
• Simulation
• Simulation & Modelling Applications
• System
• Types of Simulation Models

Module II - Monte Carlo Simulation

• A Game of Chance - Monte Carlo Simulation
• Characteristics of a Monte Carlo Simulation
• Finding pi Using Monte Carlo Simulation
• Monte Carlo Simulation in Python
• Sleepless in Seattle Experiment
• Steps in a Monte Carlo Simulation
• The Choice Function
• The Monte Carlo Method
• The Taxi Problem Experiment
• Using Monte Carlo Simulation for Inference

Module III - Discrete Events Simulation

• Discrete Events Simulation Modeling Approaches
• Discrete Events Simulation Python
• Discrete Events Simulation Use Cases
• SimPy ↪
• SimPy Elements for Discrete Events Simulation
• SimPy for Discrete Events Simulation

Module IV - Model Building

• 1. Functions vs Models
• 2. Reading Models
• 3. Model Design
• 4. Understanding the Data
• 5. Variation Analysis
• 6. Covariation Analysis
• 7. Patterns and Models
• 8. Cross Validation

Module V - Regression Models

• 1. Simple Linear Regression ↪
• 2. Multiple Linear Regression ↪
• 3. Residual Analysis ↪
• 4. Categorical Variables ↪
• 5. Interaction Terms and Polynomial Effects ↪
• 6. Model Selection and Validation ↪

Module VI - Classification Models

• 1. Introduction to Logistic Regression (Binary)
• 2. Sigmoid Function and Logit Transformation
• 3. Build the Model
• 4. Model Evaluation
• 5. Model Predictions
• 6. Multiple Logistic Regression Use Case