Jun 19, 2024  
USC Catalogue 2022-2023 
USC Catalogue 2022-2023 [ARCHIVED CATALOGUE]

Spatial Data Science (MS)

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Dornsife College of Letters, Arts and Sciences, Spatial Sciences Institute
Viterbi School of Engineering, Computer Science/Data Science


Program Director (Spatial Science): John P. Wilson, PhD

Program Associate Director: 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 must be admitted by both the Viterbi School of Engineering and the Dornsife College of Letters, Arts and Sciences.

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 USC Master of Science in Spatial Data Science provides students with the knowledge and skills to:

  • Understand and contribute toward the significant technical and societal challenges created by large location-based data environments, including their architecture, security, integrity, management and scalability.
  • Understand how spatial data can be acquired and used to support various forms of analysis, modeling and geo-visualization in large data environments.
  • Understand how artificial intelligence, machine learning and data mining can be used to augment the typical geographic information science (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.

Students complete a core set of courses to provide a foundation in information engineering, spatial analysis and thinking with their choice of electives to optimize preparation for their preferred career path and unique professional opportunities.

Students will understand the overall field of data science, the role of the analyst and/or data scientist and the domains where spatial data science skills can be applied to critical organization missions. They will understand how data management, data visualization and artificial intelligence techniques (specifically data mining and machine learning) are critical to the spatial analysis process and how these can be applied to real world challenges. Throughout their course work, students will assemble a digital portfolio of work product that is intended to help them demonstrate their capabilities and skills for the job market.

The curriculum is designed to be accessible to students with any background, including students with a geography background and no computer science knowledge as well as students with a computer science background and no geography 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 geography, geographic information science (GIS) and related disciplines will acquire formal and practical data science skills, and can request to substitute introductory courses in the spatial core 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.

For information refer to the Spatial Sciences Institute .

Degree Requirements

A minimum of 32 units with an overall cumulative GPA of at least 3.0 is required for the MS in Spatial Data Science. Students should consult with an academic adviser prior to registering for any classes.

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