BEE 6310

Environmental Statistics and Learning

Course Description

Course information provided by the 2025-2026 Catalog.

This course introduces statistical and machine learning techniques for analyzing complex datasets in the environmental sciences and engineering. It focuses on supervised learning methods, such as regression, decision trees, and neural networks, applied to real environmental data, with emphasis on both prediction and inference. Designed for students with basic statistics knowledge, the course provides a practical foundation in applied statistics and machine learning. It includes review of key mathematical and coding concepts needed to implement these tools. The goal is to build a toolbox of methods not covered in introductory courses and to help students understand when and why to use each method. Learning is hands-on, with in-class programming exercises that translate theory into application using real-world datasets.

Prerequisites CEE 3040 or ENGRD 2700; or BTRY 3010.

Enrollment Information Preferred prerequisite of MATH 2940.

Exploratory Studies (CU-SBY)

Last 4 Terms Offered 2025FA, 2024FA, 2022SP, 2021SP

Learning Outcomes

• Apply statistical and machine learning techniques in modern programming languages.
• Interpret and communicate analyses of data to support scientific discovery and advance engineering solutions in environmental fields.
• Appropriately use generative AI to assist and enhance data analysis skills to support higher-order synthesis, thinking, and learning.

View Enrollment Information