GEOSPATIAL DATA SCIENCE

Credit Hours: 3 · Undergraduate Upper-Level

This course provides a comprehensive foundation in computational tools and machine learning techniques for exploring geospatial data-driven insights across disciplines. Students will develop key skills in Python programming, data analysis, and machine learning, applying these techniques to large and complex datasets in areas like climate, hydrology, and remote sensing. Through hands-on assignments and projects, students will learn to handle multi-dimensional data, visualize patterns, and perform statistical, spatial, and machine learning analyses to address real-world environmental challenges. Graduate/Undergraduate Equivalency: EEPS 640 . Recommended Prerequisite(s): STAT 280 Mutually Exclusive: Cannot register for EEPS 440 if student has credit for EEPS 640 .