CRP 3500

Urban Data Analytics

Course Description

Course information provided by the 2025-2026 Catalog.

Urban data science is an emergent practice in urban planning and geography that combines: 1) the set of data analysis tools and methods used to understand a wide array of big data and big spatial data sources and, 2) questions of urban development, structure, complexity, theory, policy, dynamics, and outcomes. These approaches enable more spatiotemporally dynamic and granular analyses of cities and allow researchers new insight into urban dynamics. This course will provide a toolkit to speak through data, code, statistics, and visualization. Using open-source data and computational tools in Python and the Jupyter Notebook environment, we will learn how to design testable research questions, collect and prepare data, apply relevant analytical techniques, and present our process and results in an engaging and informative way. A personal laptop will be required. Undergraduates and graduates at all levels are welcome. Basic statistics is a prerequisite; no prior programming knowledge is required.

Prerequisites Basic statistics is a prerequisite.

Forbidden Overlaps CRP 3500, CRP 5500, CRP 4680, CRP 5680

Distribution Requirements (MQR-AAP)

Last 4 Terms Offered (None)

Learning Outcomes

• Use code to clean, analyze, and visualize spatial data.| Implement a descriptive or predictive analysis using appropriate data and statistical and/or computational methods.
• Clearly communicate your process and results as a data narrative through visualizations, context, and textual description.
• Use appropriate tools and methods in addressing your research topic.
• Gain knowledge of machine learning, big data, and their application in urban and environmental studies.

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