Environmental StudiesENVS 265 Data Science for the Environment

Data science is growing exponentially in size and quality, changing environmental scholarship and creating challenges of sifting, processing, and synthesizing large and diverse sources of information. In this course, students learn the fundamental practices of environmental informatics mainly using the R programming language. The workshop-style course is designed without requirement on prior experience in R. Includes environmental-related modules such as climate change, plant growth, animal predator-prey dynamics, overfishing and marine protected areas. Throughout the quarter, students use new hands-on skills to find an environmental-related topic, write a proposal, search for data, perform analyses, summarize results, and complete a final paper.


Enrollment is restricted to graduate students.