New data sources are a potential goldmine for urban planners and policy makers. But sometimes they are large, sometimes they are messy, sometimes they are awkward to access, and often they are all of these things. In this hands-on course, we’ll develop skills in scraping, processing, and managing urban data, and using tools such as natural language processing, geospatial analysis, and machine learning. We’ll use examples from transit, housing, and equity planning, and build competence in open-source tools and languages such as Python and SQL. We’ll also consider the limits to data science, and the biases and pitfalls that “big data” can entail.
Syllabus for enrolled UCLA students
- Instructor: Adam Millard-Ball
- Web design: Natasha Timmons
- Instructional designer: Mark Kayser
- Video production and editing: Paul Kimball, Barry Bishop, and Dan Alvarado
Thanks to Juan Matute and Claudia Bustamante, and for support from a UCLA Instructional Improvement Grant.