This repository contains code sprints for CEE/EAR600M002: Environmental Data Science
GRRIEn Data Science: As the volume of earth observations from satellites, global models, and the environmental IoT continues to grow, so does the potential of these observations to help scientists discover trends and patterns in environmental systems at large spatial scales. The goal of this class is to use global observationsfrom satellites, earth systems models, and the environmental IoT to generalize insights from in-situ field observations across unsampled times and locations.
When working with earth system processes, our goal is to be GRRIEn:
- Generalizable: how well do your experimental results from a sample extend to the population as a whole?
- Robust: do your statistics show good performance on data drawn from a wide range of probability and joint probability distributions?
- Reproducible: can other scientist understand and replicate your analysis and yield the same results?
- Iterpreted: based on your domain expertise, are model parameters/weights physically plausible?
For supervised Environmental learning. These exercises are intended to help you produce documented, version-controlled workflows for acquisition, processing, and GRREIn statistical analysis, and visualization of environmental data from command-line interfaces.