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IEMS 365: Analytics for Social Good

Prerequisites

GEN_ENG 150 or CS 150; GEN_ENG 231 or IEMS 302 or equivalent; IEMS 313, IEMS 310, CIV 304, MATH 368, or equivalent (CS 336 / CS 262 with instructor approval)

Description

This course explores how data, algorithms, and mathematical modeling can be used to address real-world challenges in nonprofit and public sector settings. Students work in teams on case studies such as ambulance placement, community-based healthcare delivery, and mobile food pantry systems. Alongside these applications, we develop the analytical foundations needed to design and evaluate such systems, including optimization, probabilistic modeling, dynamic programming, and data analysis. A central emphasis is on human-centered design by building models and decision tools that account for real-world constraints, equity, and access. The course is designed for students interested in applying computational tools to problems with social impact.

This course is an IE/OR elective for Industrial Engineering

LEARNING OBJECTIVES

The learning objectives for the course are to:

Formulate real-world social impact problems as analytical and optimization models, identifying key decisions and constraints.
Apply tools from optimization, probability, and data analysis to solve problems in areas such as routing, resource allocation, and facility location.
Analyze trade-offs between efficiency and fairness, and understand how modeling choices affect different demographic groups.
Communicate data-driven insights effectively to both technical and non-technical audiences through visualizations, reports, and presentations.
Evaluate ethical considerations in data-driven decision making, including issues of bias and equity.

COURSE MATERIAL

All required materials will be communicated through notes and codes, through Canvas. The course will primarily use Python for data analysis and modeling, and Tableau for visualizations. The course textbook is free and provided by the instructor.

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