University of California, Los Angeles 4 credits Urban Planning
Lecture, three hours. Preparation: basic Python programming experience or introductory Python course. New data sources are potential goldmine for urban planners and policy makers. But sometimes they are large, messy, or awkward to access, and often they are all of these things. Development of skills in scraping, processing, and managing urban data, and using tools such as natural language processing, geospatial analysis, and machine learning. Use of examples from transit, housing, and equity planning, and building of competence in open-source tools and languages such as Python and SQL. Consideration also of limits to data science, and biases and pitfalls that big data can entail. Letter grading.
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