This course introduces computational methods for real-time and large-scale analysis of Earth system data, with applications across geophysics, climate, hydrology, oceans, and environmental monitoring. Students will explore the full data workflow - from sensor-level digitization and edge computing to cloud-based high-performance processing of massive datasets. Topics include real-time signal processing, distributed computing, streaming data pipelines, basic machine learning for environmental data, and case studies in natural hazard early warning, climate diagnostics, and remote sensing. Designed for students in Earth sciences, engineering, and computational disciplines interested in building scalable, actionable insights from dynamic Earth observations.
3 units · Letter or Credit/No Credit
This course introduces computational methods for real-time and large-scale analysis of Earth system data, with applications across geophysics, climate, hydrology, oceans, and environmental monitoring. Students will explore the full data workflow - from sensor-level digitization and edge computing to cloud-based high-performance processing of massive datasets. Topics include real-time signal processing, distributed computing, streaming data pipelines, basic machine learning for environmental data, and case studies in natural hazard early warning, climate diagnostics, and remote sensing. Designed for students in Earth sciences, engineering, and computational disciplines interested in building scalable, actionable insights from dynamic Earth observations.
Offered in Spring 2027 at Stanford University.