New York University 3 credits Ctr for Urban Sci and Progress
This course explores advanced topics in urban data science, including dimensionality reduction techniques, optimization methods, and Time Series Analysis. It also covers powerful tools for big data analytics, such as Dask, MapReduce, and Multiprocessing. Additionally, the course delves into network analysis, exploring methods for studying urban networks and their structures. Other key topics include feature engineering and selection, as well as Bayesian Networks and Bayesian Inference. An introduction to game theory will also help students understand strategic decision-making in urban contexts. Through hands-on examples, students will develop the skills needed to apply these techniques to real-world urban challenges. | Prerequisite: CUSP-GX 7013 Prerequisites: CUSP-GX 7013.
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