The course introduces students to various facets of environmental data analysis such as data wrangling and visualization, exploratory data analysis, statistical modeling, version control and collaboration, and reproducible workflows. We will do all of this using R, one of the most popular, in-demand statistical programming languages in the environmental field. No prior programming experience required. Students cannot receive credit for both this and the environmental studies course of the same title. Prerequisite: MRNE 110 or permission of the instructor.