Spring 2027

Introduction to Geocomputing

Introductory course on geocomputing covering Python scripting for ArcGIS and open-source GIS utilities, including geoprocessing workflows and spatial data handling.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 8

Past student reviews · Song Gao

The class was challenging for students without prior coding experience, featuring heavy labs and programming requirements. Some found the labs astoundingly difficult, while others noted that achieving high grades required significant effort and practice.

What you’ll be able to do

  • Write shell commands to accomplish common (batch)geoprocessing tasks using popular open-source utility programs, and identify when these utilities are preferred over mainstream GIS software.

  • Develop basic skills in the Python programming language and programming concepts necessary for advanced GIS courses that require programming.

  • Use Python with open-source GIS libraries regardless of the Integrated Development Environment (IDE).

  • Demonstrate how Python can extend the capabilities of ArcGIS to solve problems difficult or impossible to address otherwise.

  • Describe major geospatial vector and raster file formats and specifications for spatial reference coordinate systems.

  • Identify online resources that support geocomputing and programming in the GIS profession.

  • Utilize newly developed GIS computation tools/libraries and platforms

Source: UW Guide

Grade history

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average GPA
letter grades
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Prerequisites

Course map

CIV ENGR/​ENVIR ST/​GEOG 377 or concurrent enrollment, or graduate/professional standing

GEOG 378 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

SONG GAO is recorded teaching in Fall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2025. 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

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records
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Student experience

the class

Past student reviews · Song Gao

Students found Song Gao’s lectures engaging and well-organized, with practical geospatial examples that connected programming to GIS concepts. The course structure was generally appreciated for its clarity and relevance to the geography field.

Recent recorded grades — Fall 2024: 3.56 GPA, 80.7% A/AB (n=57 letter grades); Spring 2025: 3.23 GPA, 76.9% A/AB (n=13 letter grades); Fall 2025: 3.71 GPA, 81.0% A/AB (n=42 letter grades).

difficulty & workload

Past student reviews · Song Gao

The class was challenging for students without prior coding experience, featuring heavy labs and programming requirements. Some found the labs astoundingly difficult, while others noted that achieving high grades required significant effort and practice.

Past student reviews · Song Gao

Instructors and TAs were responsive and supportive, often going above and beyond to help struggling students. However, some students felt the course relied too heavily on lectures and slides, wishing for more hands-on coding opportunities.

Topics

  • Geographic Information Science scripting
  • Open-source geoprocessing
  • Python for ArcGIS and open-source libraries
  • Geospatial data formats and coordinate systems

Skills

  • Python programming fundamentals
  • Shell scripting for geoprocessing
  • Using open-source GIS libraries in Python
  • Geospatial file formats and coordinate systems

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
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Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1316 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.

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Selected offering source records

No offering records for the selected term.

Raw records 
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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.

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Dataset & model

Recorded 2026-10-14

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

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "f1cc3fdaa8f6832802c21b521bc4d8c15530f1a2e4057049475d48f4a2c8979f",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}