Master of Science in Spatial Data Science
Program Director (Spatial Sciences): John P. Wilson, PhD
Program Associate Director (Spatial Sciences): Susan H. Kamei, PhD
Program Co-Director (Data Science): Yolanda Gil, PhD
The Master of Science in Spatial Data Science is a cross-disciplinary joint degree program offered by the Viterbi School of Engineering and the Dornsife College of Letters, Arts and Sciences. Students will complete a core set of courses to provide a foundation in data science and spatial analysis and choose electives to optimize preparation for their preferred career path and unique professional opportunities. Geospatial data accessibility, spatial decision support systems and geospatial problem-solving environments are revolutionizing most industries and disciplines, including health care, marketing, social services, human security, education, environmental sustainability and transportation. Spatial data science professionals draw upon engineering, computer science and spatial sciences principles to solve data-intensive, large-scale, location-based problems.
The curriculum is designed to be accessible to students with any background, including students with a spatial analysis background and no computer science knowledge as well as students with a computer science background and no spatial analysis knowledge. Students with undergraduate degrees in computer science, engineering, science or mathematics will acquire the necessary knowledge to analyze spatial data with diverse sources and purposes and can request to replace introductory data science courses with more advanced ones. Students with undergraduate degrees in the spatial analysis will acquire formal and practical data science skills and can request to substitute introductory courses in environmental studies with more advanced ones. There is no requirement of prior knowledge of programming or computer science, as the curriculum is designed with special introductory courses that are accessible to students with diverse backgrounds.
Students will learn a range of data science skills such as developing scalable data systems, using state-of-the-art software and infrastructure for data science, designing data analyses with statistical methods, and working in multi-disciplinary data science teams. They will also gain a foundation in understanding how spatial data can be acquired and used to support various forms of analysis, modeling and geo-visualization in large data environments. They will also understand and contribute toward the significant technical and societal challenges created by large location-based data environments, including their architecture, security, management and scalability. Students will understand how artificial intelligence, machine learning and data mining can be used to augment the typical geographic information sciences (GIS) concepts and workflows to intelligently mine data to provide enterprise-centric solutions for a variety of societal challenges and issues spanning the public, private and not-for-profit sectors.
Prospective students can refer to the Dornsife website for more information about the program.
All information contained here is summarized from the USC Catalogue and is considered non-official. For all rules, regulations, procedures, and outlines, please see the current academic year USC Catalogue. The USC Catalogue supersedes all other publications.
Current students follow degree requirements in effect for the academic year they began their studies at USC. If you are a current student, please refer to your STARS report or the appropriate USC Catalogue for your year. Students seeking to advance their catalogue year to follow updated curricula may contact their department advisor.