Track Overview
The Spatial Data Science track provides students with the knowledge, skills, and abilities to apply spatial data science skills to address critical issues in sustainability. Courses taught by our faculty in this track help become versed in asking questions relating to patterns and trends on or near the Earth’s surface and applying analytical methods and spatial algorithms to uncover new patterns and processes that can inform problem-solving and decision-making. There will be an emphasis on geographic information systems science, cartography, remote sensing, spatial analysis, and spatial modeling. Through a combination of classroom and hands-on learning, students will be positioned to use tools and methods to inform the development of sustainable solutions for public, private, or non-profit organizations.
Affiliated Faculty
MS in Sustainability - Spatial Data Science Track Requirements
Required courses:
- GC 501 - Seminar in Sustainability Credits: 4
- GC 502 - Proposal Design and Writing Credits: 4
and 4 credits of approved electives
Track Requirements:
Spatial Data Science GC Courses
Choose 8 credits from approved GC courses
- GC 531 - Landscape Dynamics and Analysis Credits: 4
- GC 555 - Remote Sensing Credits: 4
Spatial Data Science non-GC Courses
Choose 8 credits from approved non-GC courses
- CIS 464 - Database Management Systems Credits: 4
- CS 510 - Advanced Algorithm Design Credits: 3
- CS 514 - Software Engineering Credits: 4
- CS 582 - Advanced Database Systems Credits: 4
- PA 510 - State and Local Government Credits: 4
- PA 521 - Grant Writing for Public Administrators Credits: 2
- RE 510 - Sustainability in Outdoor Recreation Credits: 3
Capstone or Thesis 4 Credits
Choose either to complete Capstone (GC 591) or Thesis (GC 599).
