Master of Science in Spatial Data Science

The Master of Science in Spatial Data Science (M.S. SPDS) online program provides an understanding of the data science field and how spatial data science knowledge and skills can be applied to solve critical issues, engaging from the perspective of a GIS expert.

Geospatial data accessibility, spatial decision support systems and geospatial problem-solving environments are leading to geospatial data science career opportunities in healthcare, marketing, social services, human security, education, environmental sustainability, transportation and more. This program uniquely features courses from both the Viterbi School of Engineering and the USC Spatial Sciences Institute.

Earn your Master of Science in Spatial Data Science online from the USC Spatial Sciences Institute.


Curriculum (32 units)

Year 1
Semester 1
DSCI 549: Introduction to Computational Thinking and Data Science (4)
SSCI 581: Concepts of Spatial Thinking (4)

Semester 2
DSCI 510: Principles of Programming for Data Science (4)
SSCI 586: Spatial Programming and Customization (4)

Year 2
Semester 1
DSCI 550: Data Science at Scale (4)
SSCI 575: Spatial Data Science (4)

Semester 2
Choose one Data Science course for one 4-unit elective:
CSCI 587: Geospatial Information Management; DSCI 551: Foundations of Data Management; DSCI 552: Machine Learning for Data Science; DSCI 553: Foundations and Applications of Data Mining; DSCI 554: Information Visualization; DSCI 555: Interaction Design and Usability Testing; DSCI 560: Data Informatics Professional Practicum

Choose one Spatial Science course for one 4-unit elective:
SSCI 582: Spatial Databases; SSCI 583: Spatial Analysis; SSCI 591: Web and Mobile GIS

Career Opportunities for M.S. SPDS Graduates

Graduates of the M.S. in Spatial Data Science online program are prepared to make an impact in the workplace with their understanding of:

  • The significant technical and societal challenges created by large location-based data environments, including architecture, security, integrity, management and scalability.
  • How spatial data can be acquired and used to support various forms of analysis, modeling and geo-visualization in large data environments
  • How artificial intelligence, machine learning and data mining can augment typical geographic information science (GIS) concepts and workflows to intelligently mine data, providing enterprise-centric solutions for a variety of societal challenges and issues across all sectors.