This is a junior-level undergraduate course at Virginia Tech, Blacksburg, VA. It is designed as a required class for the Environmental Data Science major and an optional course for the Computational Modeling and Data Analytics major. Students are expected to have prior experience with R programing.
The materials are developed by Dr. Quinn Thomas and build on materials developed by many others (see acknowledgments in the specific models)
Each repository is a module in the course. Using GitHub Classroom, students fork the repository into private repositories that exist in a course-specific organization.
The materials are open-education materials that are available through an MIT or CC-BY license.
git-rmd-intro: a module that helps students get set up with GitHub, Git, and Quarto.tidyverse-intro: a module that introduces/reviews basic data science components through tidyverse functions. Uses tabular data on reservoir water quality in southwest Virginia.climate-change: plotting. Uses NOAA and NASA data on global temperature and atmospheric CO2lake-ice: regression, modeling, and reading Excel files. It uses measurements of ice cover at a lake.water-quality: functions, iteration, APIs. Uses USGS river water quality data.land-carbon: joining tables, databases. Uses NEON forest inventory data.machine-learning: machine learning with tidymodels package. Uses NEON forest carbon data.environmental-justice: geospatial analysis with raster cubes and polygons. Uses remotely sensed NDVI and maps of historical redlining.dashboard-lake-ice: dashboards and review of other module skills. It uses global measurements of ice cover.
eco-forecast: ecological forecasting. Designed around a set of Macrosystems EDDIE modules.