Co-Instructor
Essential Math in Environmental Data Science
Master of Environmental Data Science (MEDS)
Artwork by Allison Horst
Course Description
Quantitative skills are critical when working with, understanding, analyzing and gleaning insights from environmental data. In the intensive EDS 212 course, students will refresh fundamental skills in basic math (algebra, uni- and multivariate functions, units and unit conversions), derivative and integral calculus, differential equations, linear algebra, and reading, writing and evaluating logical operations.
The goal of EDS 212 (Essential Math in Environmental Data Science) is to prepare incoming MEDS students with quantitative methods, skills, notation and language commonly used in environmental data science and required for their data science courses and projects in the program. By the end of the course, students should be able to:
- Perform the following by hand and in R: convert units, basic algebra and working with logs and exponentials; write, interpret and evaluate univariate and multivariate functions; basic derivatives and integrals with univariate and multivariate functions; solve simple differential equations; basic operations with scalars, vectors and matrices; writing and evaluating logicals
- Explain and share examples for how all topics in EDS 212 are useful and used in applied environmental data science
- Interpret examples of applied math & models from environmental science case studies
- Work with peers to solve group tasks, then communicate the process of problem solving to the rest of the class
Teaching Team
Acknowledgements
EDS 212 was originally developed and taught by Allison Horst. This new website houses materials which are heavily reused, adapted from, and inspired by Allison’s original work.