Syllabus

DANL/ECON 399: Environmental Data Science

Course information

Item	Details
Semester	Fall 2026
Meeting pattern	Mondays and Wednesdays, 12:30–1:45 PM
Classroom	South 227B (instructor’s office)
Office hours	Mondays and Wednesdays, 1:45–3:15 PM, South 227B
Format	Individualized study with the instructor
Instructor	Byeong-Hak Choe
Email	bchoe@geneseo.edu

Course description

This individualized course pairs students with the instructor to study environmental and natural resource policy using data. Students will practice data transformation, visualization, spatial data analysis, and machine learning in R on environmental data, while a parallel research track develops literature and policy inquiry. Students move from research question to analysis plan and implementation. Assessment includes a research proposal, Research Kick-off Report, progress report, final comprehensive exam, and research paper.

How the course works

The course develops along two parallel tracks:

Technical track: transform, visualize, map, and model environmental data in R.
Research track: study the relevant literature, clarify the policy setting, and refine a meaningful research question.

The tracks meet in a reproducible analysis and research paper that interprets findings in their environmental and natural resource policy context. Because the course is individualized, the precise sequence and emphasis may adapt to the research question, data, and progress of the project.

The usual weekly rhythm is:

Monday — research and methods studio: frame the question, read evidence, make design decisions, and interpret results.
Wednesday — applied R lab: acquire, transform, visualize, map, model, and document the project data.

Learning goals

By the end of the course, students should be able to:

Formulate a focused research question in environmental or natural resource policy.
Find and engage relevant literature and identify key policy debates.
Develop a feasible research design and analysis plan.
Transform and document environmental data in R using a reproducible workflow.
Design effective visualizations for exploration and communication.
Work with spatial data and apply appropriate spatial analysis methods.
Formulate, evaluate, and interpret a suitable machine-learning analysis.
Synthesize research, methods, findings, limitations, and policy implications in clear writing.

Assessment

Research proposal

The proposal establishes the research question, motivation, relevant policy and literature context, proposed data, and analysis plan. Scope and timing will be set with the instructor.

Research Kick-off Report

The Research Kick-off Report demonstrates that the proposed study is feasible with real data. It includes a refined question and policy motivation, a focused literature map, documented data provenance and unit of analysis, a reproducible data intake, a data-quality audit, at least one question-led exploratory visualization, a preliminary map when geography matters, and an initial analysis and validation plan. It is due Tuesday, October 20, 2026—one week after Fall Break.

Progress report

The progress report documents completed work, early evidence, technical or research obstacles, decisions made, and the plan for the remaining study. Scope and timing will be set with the instructor.

Final comprehensive exam

There is no midterm exam. A comprehensive final exam will evaluate synthesis and application of the research and data-science methods developed during the course. It will take place during the December 9–15 final-examination period; the exact date, time, and format are TBD.

Research paper

The research paper presents the final research design, analysis, findings, limitations, and interpretation in the environmental or natural resource policy context. Scope and timing will be set with the instructor.

Evaluation criteria

Student work will be evaluated using four criteria:

Technical implementation in R — effective use of data transformation, visualization, spatial analysis, and machine learning.
Grasp of the research question — engagement with relevant literature and key debates in environmental and natural resource policy.
Research paper — quality of research design, analysis, writing, and integration of findings with the policy context.
Comprehensive exam — synthesis and application of research and data-science methods covered during the course.

Grade weights and any due dates not fixed above will be established with the instructor and communicated through Brightspace.

Technical environment and workflow

Analytical work will be completed in R. Core tools may include tidyverse for transformation, ggplot2 for visualization, and sf for vector spatial work. Machine-learning code will use direct, model-specific workflows—base stats plus packages such as glmnet, rpart, ranger, or xgboost when appropriate—rather than a modeling framework. The terra package is optional and will be introduced only if the selected project needs raster data such as land cover, elevation, or satellite imagery. Interactive maps may use leaflet or tmap; Google Maps Platform may be used as a bounded extension when its basemaps, geocoding, routes, places, or environmental APIs materially support the research question. Work should be reproducible, clearly documented, and organized so that data preparation, analysis, and outputs can be reviewed together.

Tentative course map

The course meets Mondays and Wednesdays, 12:30–1:45 PM, in South 227B. The semester map uses Monday meetings for research and methods studios and Wednesday meetings for applied R labs.

The official SUNY Geneseo calendar identifies September 7 as Labor Day, October 10–13 as Fall Break, November 25–28 as Thanksgiving Break, December 7 as the last day of classes, and December 9–15 as the final-examination period. See the official academic calendar for authoritative campus dates.

Working syllabus

This syllabus is a working document. The project focus, weekly emphasis, assessment details, and due dates may be adjusted in consultation with the student. Updates will be communicated during course meetings and/or through Brightspace.