Foundations of Behavioral Data Science

This unit introduces core principles at the intersection of behavioral economics, psychology, and data science, covering the history and development of behavioral economics, the psychology of decision-making, and the role of data, models, and algorithms in uncovering consumer behavior. Students will learn to pose actionable research questions, simplify complex phenomena into analyzable components, and begin translating behavioral insights into ethical, customer‑centric analytics and intervention plans that prepare them for deeper technical work in subsequent units.

Learning Objectives

• Analyze historical and contemporary developments in behavioral economics and decision psychology to explain how cognitive biases and economic factors influence consumer behavior using at least three case examples
• Apply data-driven inquiry by formulating clear research questions, identifying relevant behavioral datasets, and designing a simplified analytical plan (features, metrics, and evaluation criteria) to test behavioral hypotheses
• Demonstrate the selection and interpretation of appropriate models and algorithms (e.g., descriptive statistics, clustering, classification, basic predictive models) to uncover patterns in behavioral data, including articulation of model limitations and ethical considerations
• Evaluate and propose customer-centric interventions that integrate data analytics, machine learning, or AI with behavioral insights (including nudges), and produce a justified prototype plan with measurable success criteria