ENST431

Modern environmental science relies on data from many sources such as field observations, sensors, satellites, and models to understand how the planet is changing. This course introduces students to the core ideas and practices of environmental data science through theory and practice. Students will learn how to explore, visualize, and interpret environmental data. They will work with spatial datasets to map environmental patterns, build models to describe relationships among different environmental processes, and analyze time series. Emphasis is placed on developing algorithmic thinking, creativity in problem solving, and clear communication of results. By the end of the course, students will be able to work confidently with diverse datasets and models to solve real-world environmental problems. No prior programming experience is required, but familiarity with the R programming language is strongly recommended.