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IEMS 365: Analytics for Social Good


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Prerequisites

IEMS 303 and IEMS 313, Junior or Senior standing

Description

This course in humanitarian and nonprofit logistics will explore the challenges and
opportunities of achieving social good in the age of analytics. Students will work in teams on a
series of case studies ranging from advanced technology for disaster response preparedness,
improved decision-making frameworks for community-based health care providers, and the
design of mobile food pantries. Alongside these applications, we will also explore the
mathematical models underlying these examples, studying their probabilistic properties and
designing tools to optimize them efficiently. This course is designed for juniors and seniors
interested in analytics and humanitarian, nonprofit, and public sector operations with a
background in optimization and statistics (see new prerequisites below). The course format
will combine weekly lectures by the professor and interactive case study team sessions, led
by the professor and the teaching assistant.

● This course is an IE/OR elective for Industrial Engineering


LEARNING OBJECTIVES

The learning objectives for the course are to:

  • Expose students to pressing issues in humanitarian, nonprofit, and public sector operations through a series of case studies
  • Develop an understanding of descriptive, predictive, and perspective analytics approaches for these problems, including a deeper exploration of the mathematical foundations underlying these methods
  • Examine the ethical considerations and challenges of implementing analytical solutions in real-world scenarios, emphasizing the importance of equity and transparency
  • Engage students in interactive discussions of analytics and humanitarian, nonprofit,and public sector

COURSE MATERIAL

All required materials will be communicated through notes and codes, via Canvas. The course
will primarily use Python for data analysis and modeling, and Tableau for visualizations.


PREREQUISITES


New this year: this course requires IEMS 303 and IEMS 313; Junior or senior standing. Note
that there is no application this year.


TOPICS

  • Data analytics tools
  • Routing models
  • Evacuation planning
  • Disaster response and preparedness
  • Facility location models
  • Fair resource allocation
  • Algorithmic bias in machine learning