Introduces the field of computational urban science, focusing on methods to collect and analyze urban data. Covers techniques such as geographic information systems, network science, machine learning, spatial models, and causal inference to examine city dynamics and inform urban planning, policy, and research. Explores the application of these methods to real-world urban datasets, including mobile phone data, social media, and transactional data, enabling critical insights into the complexities of urban systems.
Offering history
Term
Sections
Enrolled
Capacity
Full
Open seats/section
Spring 2026
1
7
15
47%
8.0
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 100% · W 0% · Th 0% · F 0%
Common patterns: T (100% of sections)
— in patterns, R means Thursday