Spring 2027

Using R for Soil and Environmental Sciences

Applies R software and data science techniques to analyze soil and environmental data from in situ, ground-based, and remote sensing platforms, covering processing, modeling, and case studies.

offering recorded3 credits
Recorded instructors · Spring 2027 Jingyi Huang

Summary

1 / 3

Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=24 letter grades); Spring 2026: 3.97 GPA, 96.8% A/AB (n=31 letter grades).

What you’ll be able to do

  • Use R software for basic data manipulation, processing, and visualization.

  • Use R software for basic statistical analysis and hypothesis tests.

  • Use R software for spatial and temporal analysis on soil and environmental datasets.

  • Become familiar with the use of electromagnetic induction, portable visible near-infrared spectroscopy, and portal X-ray fluorescence spectroscopy for estimating soil and environmental variables in the lab and in the field.

  • Build machine learning models using R software for mapping and predicting soil and environmental variables in space and time.

  • Access, process, and build models using various open-source soil and environmental datasets from USDA, USGS, NASA, and other sources.

  • Become competent in the oral presentation.

  • Apply the various R packages to your own research datasets for solving problems in your own disciplines.

  • Explain the social, economic, and environmental dimensions of the sustainability challenges of soil and environmental monitoring and management.

  • Analyze sustainability issues and/or practices on soil and environment using a systems-based approach.

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, orF&W ECOL/​STAT 571, or HORT 571 prior to Spring 2025) and (BSE 301,ENVIR ST/​CIV ENGR/​GEOG 377,LAND ARC/​ENVIR ST/​G L E/​GEOG/​GEOSCI 371, or FW ECOL 371 prior to Summer 2026), or graduate/professional standing

SOILSCI 585

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

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=24 letter grades); Spring 2026: 3.97 GPA, 96.8% A/AB (n=31 letter grades).

Historical instructors & teaching patterns

JINGYI HUANG is recorded teaching in Spring 2023, 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 001Online Only0 / 500

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

Meeting source records

No records available.

Student experience

the class

Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=24 letter grades); Spring 2026: 3.97 GPA, 96.8% A/AB (n=31 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Soil and environmental data processing, manipulation, and modeling
  • In situ, ground-based, and remote sensing data collection
  • Machine learning for mapping and predicting soil variables
  • Sustainability challenges and systems-based approaches in soil monitoring

Skills

  • Data manipulation, processing, and visualization using R
  • Statistical analysis and hypothesis testing in R
  • Building machine learning models for spatial and temporal prediction
  • Using spectroscopy and electromagnetic induction for soil variable estimation
  • Spatial and temporal analysis of soil and environmental datasets

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
SOILSCI 585 · Spring 2027

Using R for Soil and Environmental Sciences

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": "1274:908:026044",
    "course_id": "SOILSCI 585",
    "course_uid": "course_cb0186b8573824eccce2a076",
    "term_id": "1274",
    "source_course_id": "026044",
    "source_subject_id": "908",
    "title": "Using R for Soil and Environmental Sciences",
    "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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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": "597565bc7f1b9650be47a67433546eb27d8b573dcebf53611fcc05f983d430bf",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}