BEE 4110/6110 Hydrologic Engineering in a Changing Climate

This course introduces methods in hydrologic engineering to assess and cope with climate variability and change. The course covers both statistical and physical approaches to analyzing and modeling hydrologic systems. Students learn the core concepts of traditional statistical analyses in hydrology, and also learn the limitations of these approaches in a changing climate. Students become familiar with physical modeling approaches to understand hydrologic response under future climate projections and their limitations. They learn to recognize the rapidly changing nature of the field of hydrologic engineering as it tries to adapt to the impacts of climate change. Course topics : extreme event frequency analysis; trend detection; water balance modeling; hydrologic simulations under projected climate change.

BEE 4310/6310 Environmental Statistics and Learning

This course introduces relatively simple but powerful techniques in statistics and machine learning needed to analyze and model complex datasets frequently encountered in the environmental sciences and engineering. The course covers both supervised and unsupervised learning, including linear regression, penalized regression, generalized linear models, local regression, decision trees and random forest, principal component analysis, and clustering. These topics are introduced through applications to data from various environmental fields, with an emphasis on both prediction and inference. The goal is to provide students with a toolbox of methods not taught in more introductory statistical courses, but also to ensure that students feel comfortable understanding when to use which methods and why without viewing them as a “black box”. The course serves as a first course in applied statistics and machine learning for students with only a basic knowledge of probability and statistics. The course will provide a review of the mathematical concepts and coding skills needed to understand and use the techniques presented. Students will learn by doing, with ample time in class to practice translating theory to application through programming exercises on real environmental datasets.

 

These courses are supported by DataCamp, an interactive platform for data and AI skills. DataCamp Classrooms offers free licenses for educators and students, with 500+ courses and 100+ real-world projects to learn R, Python, SQL, and more.