Data science is rapidly transforming environmental scholarship. Environmental data are growing exponentially in size and quality at an unprecedented rate. This data deluge brings new challenges in sifting, processing, and synthesizing large, diverse sources of information. In this course, students will learn the fundamental practices of environmental data science, primarily using the R programming language, complemented by other computing tools such as SQL, Git, and Python. The class will cover a wide range of computing topics, supported by environmental case studies on climate change, plant growth, predator-prey dynamics, overfishing, and marine protected areas. Each topic will introduce new datasets and questions, leading to new hands-on skills. Students will use these skills throughout the course to design a project, draft a proposal, search for data, conduct analyses, communicate results, and complete a final paper.