Comprehensive introduction to ecological engineering and design. Concepts include sustainability, wetlands, ecosystem services, nutrient cycling, watershed management, and nature-based solutions.

(RE) Prerequisite(s) : EF 152 or EF 158; CHEM 122 and CHEM 123, or CHEM 128

This course provides students with the fundamentals of data science and modeling for analyzing environmental, ecological, and agricultural systems using the open-source software R. Note that prior programming experience is not required. This course is organized into the following sections: (1) introduction to programming in R, including the development of skills for cleaning environmental data, summarizing data, and creating visualizations, (2) overview of data-based and process-based modeling approaches, (3) applications, evaluation, and challenges of modeling in relation to environmental systems. Students will gain a broad understanding of different analytical tools and learn to apply such methods to agricultural and ecological data. This course is designed for students in a natural resources and life sciences discipline. Credit Restriction: Students may not receive credit for both BSE 464 and BSE 564
(RE) Prerequisite(s) : MATH 125 or MATH 141; CHEM 122 and CHEM 123; BSE 231 or CHEM 132 and CHEM 133; STAT 201 or STAT 251

Current trends and problems in biosystems engineering.

Repeatability: May be repeated. Maximum 6 hours.

Topics in engineering for the characterization, conservation, and protection of soil, water, and air resources in spite of human activities such as off-road vehicle use, agriculture, mining, construction and land development, or waste application.

Repeatability: May be repeated. Maximum 12 hours.

Provides students with the fundamentals of data science and modeling for analyzing environmental, ecological, and agricultural systems using the open-source software R. Note that prior programming experience is not required. Course is organized into the following sections: (1) introduction to programming in R, including the development of skills for cleaning environmental data, summarizing data, and creating visualizations, (2) overview of data-based and process-based modeling approaches, (3) applications, evaluation, and challenges of modeling in relation to environmental systems. Students will gain a broad understanding of different analytical tools and learn to apply such methods to agricultural and ecological data. Designed for students in a natural resources and life sciences discipline. Credit Restriction(s): Students cannot receive credit for both BSE 464 and BSE 564.
Recommended Background: General chemistry, one semester of calculus, one semester of statistics.
Registration Restriction(s): Minimum student level - graduate.