Master of Science in Urban Analytics
The Master of Science in Urban Analytics degree is designed to give graduates a core of computing, planning, and data analysis and visualization skills to identify, analyze, and solve urban problems; to integrate those skills in an interdisciplinary way that other, single-discipline-oriented urban analytics degrees might not; and to provide depth in urban problems that can be addressed through data analytics.
The program is interdisciplinary between the School of City and Regional Planning (SCaRP, within the College of Design), the Schools of Computational Science and Engineering and Interactive Computing (CSE and IC, both within the College of Computing), and the Stewart School of Industrial and Systems Engineering (ISyE, within the College of Engineering). Each of the four units (SCaRP, IC, CSE, and ISyE) provides expertise in a different facet of urban analytics, with interdisciplinary coordination achieved by having faculty cooperate on the development and revision of course content, especially in the core and required courses.
| Code | Title | Credit Hours |
|---|---|---|
| Urban Systems Core Courses | ||
| CP 6552 | Design of Smart Urban Systems | 3 |
| CP 8803 | Special Topics in Transportation Planning (Introduction to Urban Analytics ) | 3 |
| Capstone and application of urban analytics in practice | ||
| CP 6960 | Urban Analytics Capstone Project | 1 |
| CP 6962 | Urban Analytics Capstone Project | 5 |
| Courses in spatial analysis | 6 | |
| Advanced Geographic Information Systems | ||
| Socioeconomic GIS | ||
| Environmental Analysis Using GIS | ||
| Transport & GIS | ||
| Courses in computational statistics | 6 | |
| Computational Problem Solving for Scientists and Engineers | ||
| Computing for Data Analysis: Methods and Tools | ||
| Web Search and Text Mining | ||
| Computational Data Analysis: Learning, Mining, and Computation | ||
| Computational Statistics | ||
| Theoretical Statistics | ||
| Statistical Modeling and Regression Analysis | ||
| Courses in modeling and visualization | 6 | |
| Intro Analytics Modeling | ||
| High Performance Computing | ||
| Data and Visual Analytics | ||
| Data Visualization: Principles and Applications | ||
| Information Visualization | ||
| Modeling and Simulation: Foundations and Implementation | ||
| Total Credit Hours | 30 | |