Courses

Introductory Statistics for Environment and Sustainability

(formerly Natural Resource Statistics)

The fields of environment and sustainability increasingly rely on quantitative methods to understand complex systems, test hypotheses, and develop solutions to real-world problems. A solid grasp of statistical analysis is therefore crucial for professionals in these areas. This course introduces students to applied statistics—emphasizing practical application over mathematical theory. In addition, students gain hands-on experience with R, a widely used statistical programming language in environmental sciences.

Environmental Data Science

Data science is rapidly transforming environmental scholarship. Environmental data is growing in both volume and quality at an unprecedented rate, creating new challenges in processing, synthesizing, and analyzing large and diverse information sources. In this course, students learn key practices of environmental informatics, primarily using the R programming language. This workshop-style class features environmentally focused modules, each introducing new datasets and analytical questions. Throughout the course, students apply their skills to an environmental topic of their choice, culminating in a data analysis paper.

Analysis and Modeling of Environmental Data

This course focuses on advanced statistical methods for analyzing and modeling environmental data, emphasizing both frequentist and Bayesian approaches. While students will use R for data analysis, the primary emphasis is on understanding statistical concepts, methodology, and interpretation. Hands-on exercises and a project-based component help students apply rigorous statistical techniques and effectively communicate their findings in the context of environmental science.

General Ecology

Ecology is the study of the relationships between living organisms, including humans, and their physical environment. This course is designed for students to understand the general ideas and concepts of ecology, to be able to integrate information, formulate solutions, and solve ecological problems in modern life. We emphasize connections with mathematical, physical, and chemical processes, as well as the application of ecology to conservation and global change issues.

Landscape Ecology

Landscape ecology embraces a diverse range of topics concerned with the causes and consequences of spatial heterogeneity and patterns in natural systems as well as those dominated by human activities. This course is intended to provide a foundational understanding of how landscape pattern is generated and why it matters to populations, communities, and ecosystem processes. Along the way, it requires considerations of scale and pattern in general. The understanding is built on conceptual models and reinforced with biophysical, statistical, or ecological simulation models as appropriate.

Design and Analysis of Biological Experiments

This course addresses how to set up and how to draw conclusions from biological experiments. It introduces basic theories in statistics, interwoven with data analysis using software packages. Students learn to design statistically sound data collection in observational or experimental studies. To answer given research questions, students choose among modern statistical tools and analyze data using software. Students also learn to effectively present results using statistical graphics. This class particularly focuses on ecological and environmental data.