Environmental Data Analysis

Covers methods for analyzing time series and spatial data within the context of environmental datasets (both air and water). Techniques covered include regressions, correlations, interpolations, spectral analysis, empirical orthogonal functions, clustering, and random forest. Focus on practical applications. Culminates in a final project where students apply these techniques to a research question of their choice. May be repeated once for credit.

Letter grading.