Introduction to Urban Data Science: Theory, Applications, and Practice

4.0 credits · Graduate

Urban data science combines questions of urban development, structure, complexity, theory, policy, and dynamics with a set of computational tools and big data. This course introduces core theoretical perspectives in urban studies, spatial analysis, and critical data studies, and considers how data, models, and algorithms produce particular ways of knowing and governing cities. Using Python and open-source urban datasets in the Jupyter Notebook environment, students will work on hands-on applied exercises and projects which emphasize framing theoretically grounded and data-driven questions, analyzing and visualizing data, and communicating results for planning and policy contexts. This course also foregrounds ethical issues in urban data science, including data bias, privacy, equity, and the responsible use of artificial intelligence. A personal laptop is required.