INFO 5430 - Urban Data
Fall. 3 credits. Letter grades only.
Prerequisite: INFO 5410 . Enrollment limited to: Cornell Tech students. Offered in New York City at Cornell Tech.
E. Pierson.
This course provides a broad overview of the opportunities and challenges related to urban data and helps familiarize students with key datasets and the tools and methodologies to visualize and analyze them.
The course will introduce a framework to reason about urban data and will present various tools and methodologies to process the data, including data mining, machine learning, GIS, network analysis, simulation, agent-based modeling, data visualization. Traditional Big Data challenges will be reviewed and the associated challenges specific to urban data such as quality, privacy, bias, and data governance will be highlighted. Students will also be introduced to the relevant optimization and simulation models so as to enable them to leverage these tools for data-driven decision-making and creating policy.
Outcome 1: Discover relevant open data datasets to answer questions related to a given urban data issue.
Outcome 2: Think critically about new kinds of urban data in terms of collection, storage, and processing; students will also be able to assess issues related to privacy and bias.
Outcome 3: Ingest, process and visualize urban data including tabular data, GIS data or graph data.
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