Description: Introduction to the modeling and analysis of environmental data with a rigorous grounding in probability, optimization, and understanding of underlying physical mechanisms. This course will place a particular focus on the iterative process of building, computing, and critiquing predictive models of environmental processes. Assignments will use the Julia programming language, which will be introduced in the course. Recommended Prerequisite(s): A first course in statistical modeling (STAT 312, STAT 315, CEVE 313, or equivalent) and linear algebra (CAAM 335, MATH 355, or equivalent) is strongly recommended. Additional experience with statistics, optimization, programming, and/or machine learning will prove helpful.