Study a changing planet through data.
Move from an environmental policy question to a reproducible analysis in R—combining research, visualization, spatial evidence, and machine learning.
One question. Two tracks. One integrated study.
The technical and research tracks develop in parallel, then meet in an analysis that connects evidence to environmental policy.
01Frame · Research trackFind the consequential questionRead the literature, identify a live policy debate, and turn a broad environmental concern into a focused research question.→ 02Prepare · Data labBuild a defensible data stackPair one anchor environmental dataset with only the contextual layers needed to study the question.→ 03Analyze · Technical trackVisualize first. Model second.Use graphics and maps to understand the evidence, then compare a baseline with one justified machine-learning extension.→ 04Synthesize · Integrated trackConnect findings to policyMove from proposal and research kick-off through progress evidence, the final comprehensive exam, and a policy-grounded paper.→
Plan around the semester’s fixed points.
Open the right workspace.
Use the semester map for sequence, the data and methods pages for implementation, and the research and milestone guides to keep the final study coherent.
Methods pathData transformation, visualization, spatial analysis, and machine learning in R. Data labCurated environmental sources, project combinations, and a cost-controlled Google Maps option. Research trackLiterature, policy inquiry, question development, and research design. MilestonesProposal, research kick-off, progress report, final comprehensive exam, and research paper. Semester mapThe Monday research studios, Wednesday R labs, campus breaks, and assessment flow. SyllabusCourse description, learning goals, evaluation criteria, and meeting details.