Course Syllabus

Module 1

1. Introduction to Decision Science

• Overview of the decision-making process
• Rationality and decision theory
• Types of decisions and decision problems
• Decision trees and probability theory

Module 2

2. Decision Analysis and Data Uncertainty

• Structuring decision problems
• Decision criteria and utility theory
• Multi-criteria decision analysis
• Sensitivity analysis and risk assessment
• Sources of uncertainty and risk
• Probability distributions and statistical inference
• Monte Carlo simulation and bootstrapping

Module 3

3. Decision-making Frameworks and Behavioral Decision Making

• Cost-benefit analysis and net present value
• Decision-making under risk and uncertainty
• Game theory and strategic decision making
• Cognitive biases and heuristics
• Prospect theory and loss aversion
• Framing and its effect on decision making

Module 4

4. Data Analytics, Decision Making, and Ethics

• Introduction to data analytics and machine learning
• Regression analysis and predictive modeling
• Decision trees and random forests
• Ethical considerations in decision making
• Decision making and social responsibility
• Sustainability and decision making