Choose a Catalog:  

2026-2027 Graduate Catalog (20273)

PDF of Environmental Studies Courses

Environmental Studies Courses

All Environmental Studies Courses

ENVR 5040 Environmental Economics (3 credits)
ENVR 5050 Geochemistry (3 credits)
ENVR 5110 Environmental Chemistry (3 credits)
ENVR 5150 Outdoor Field Skills (2 credits)
ENVR 5200 Wastewater Treatment (3 credits)
ENVR 5210 Environmental Law and Policy (3 credits)
ENVR 5220 Sampling and Analysis (4 credits)
ENVR 5230 Air Pollution Technology (4 credits)
ENVR 5240 Waste Management (4 credits)
ENVR 5260 Risk, Resilience and Sustainable Community Development (3 credits)
ENVR 5300 Environmental Management and Safety (3 credits)
ENVR 5400 Environmental Microbiology (3 credits)
ENVR 5500 Environmental Toxicology (4 credits)
ENVR 5600 Environmental Justice and Sustainability (3 credits)
ENVR 5610 Sustainability: Theory and Practice (4 credits)
ENVR 5700 Natural Resource Management (3 credits)
ENVR 5710 Indigenous Environmental Knowledge: Global Perspective (3 credits)
ENVR 5720 Food Sovereignty, Health & Indigenous Environments (3 credits)
ENVR 5730 Sustainable Communities: Local Indigenous Perspective (3 credits)
ENVR 5740 Environment, Wellness & the Sacred Connection to Place (3 credits)
ENVR 5750 Sustainable Communities: Global Indigenous Perspective (3 credits)
ENVR 5800 Sustainability Analytics & Modeling (3 credits)
ENVR 5840 Wetlands Ecology (3 credits)
ENVR 6250 Advanced Environmental Studies (3 credits)
ENVR 6400 Research and Project Design (3 credits)
ENVR 6410 Environmental Data Science and Modeling (3 credits)
ENVR 6500 Advanced Graduate Project I (2 credits)
ENVR 6600 Capstone Report (2 credits)
ENVR 6700 Graduate Sustainability Seminar (1-3 credits)
ENVR 6790 Environmental Project Management (3 credits)
ENVR 6880 Seminar (1 credits)
ENVR 6899 Capstone (3 credits)
ENVR 6910 Directed Independent Study (1-9 credits)
ENVR 6920 Directed Group Study: Seminar (2 credits)
ENVR 6990 Thesis (1-6 credits)

ENVR 6410 Environmental Data Science and Modeling (3 credits)

This graduate-level course develops analytical reasoning and quantitative skills for environmental and biological sciences. It provides a foundation in inferential statistics, extends into regression and generalized linear modeling, and introduces multivariate and machine-learning techniques commonly used in research across ecology, fisheries, environmental monitoring, field toxicology, and bench biology. Emphasis is placed on experimental design, reproducibility, visualization, and interpretation of complex datasets. Students will learn to implement analyses using R and produce fully reproducible reports. The course concludes with an introduction to Bayesian reasoning and Monte Carlo simulation as tools for uncertainty analysis.
Common Course Outline