Spring 2027

Environmental Data Science

Environmental Data Science introduces machine learning and programming tools for analyzing, preparing, and visualizing Earth and environmental science datasets.

offering recorded3 credits
Recorded instructors · Spring 2027 Min Chen

Summary

1 / 3

Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=16 letter grades); Spring 2026: 3.95 GPA, 97.4% A/AB (n=39 letter grades).

What you’ll be able to do

  • Demonstrate introductory skills in using collaboration technology (e.g. Jupyter Notebooks) to write, edit, and run programs in a scientific programming language (e.g. Python)

  • Recognize, read, write and use common environmental dataset formats

  • Use a scientific programming language (e.g. Python) to read and process environmental data

  • Produce visualizations of environmental data, including basic scientific charts, statistics, and maps

  • Understand fundamentals of modern machine learning algorithms and gain experience of practical use of them

  • Solve real-world data science problems individually and in teams

  • Identify the frontiers in real-world environmental science challenges and how data science can help

  • Identify a problem in environmental science that may be solved or better understood through data science, provide a basic visualization or analysis of a data set associated with that problem, and communicate findings effectively

  • Develop in-depth spatial and/temporal analyses using advanced data science tools (such as machine learning) and visual datasets related to your own research or anticipated area of research that meet the standards of scientific journals, critically evaluate your findings, and situate them within the larger context of current research literature

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

STAT 240, 301, 324, 371, or graduate/professional standing

F&WECOL 458

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=16 letter grades); Spring 2026: 3.95 GPA, 97.4% A/AB (n=39 letter grades).

Historical instructors & teaching patterns

MIN CHEN is recorded teaching in Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 500

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=16 letter grades); Spring 2026: 3.95 GPA, 97.4% A/AB (n=39 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Machine learning techniques for numerical modeling and data analysis
  • Programming tools for analyzing, preparing, and visualizing environmental datasets
  • Real-world applications of environmental data science concepts

Skills

  • Using Jupyter Notebooks to write, edit, and run programs in Python
  • Recognizing, reading, writing, and using common environmental dataset formats
  • Producing visualizations of environmental data, including charts, statistics, and maps
  • Understanding and practically applying modern machine learning algorithms
  • Communicating data science findings related to environmental problems effectively

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
F&WECOL 458 · Spring 2027

Environmental Data Science

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:396:026535",
    "course_id": "F&WECOL 458",
    "course_uid": "course_4125898f74a0419b4957be76",
    "term_id": "1274",
    "source_course_id": "026535",
    "source_subject_id": "396",
    "title": "Environmental Data Science",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "3415240878f67e0015fc0609a32e54517589789d5e3bf5243003de8dd888f6be",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}