Fall 2026

Advanced Geocomputing and Geospatial Big Data Analytics

Introduction to theory, techniques, and analytical methods for geospatial big data using advanced Python.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Song Gao: The course is described as challenging, with one student noting it was among the most difficult they have taken. Preparation involves engaging with Python3, Jupyter Notebooks, Pandas, and GeoPandas for spatial analysis.

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

GEOG 378,COMP SCI 220, or graduate/professional standing

GEOG 573

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

Prerequisite text tree

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Song Gao: Song Gao is described as a fantastic and responsible instructor who provides useful academic materials and patient support. Reviewers highlight the course's appropriate mix of technical geocomputing and real-world applications, noting that lectures and labs are highly useful for learning GIS and data science concepts.

SONG GAO is recorded teaching in Spring 2020, Spring 2022, Spring 2023, 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

Fall 2026

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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

No records available.

Student experience

the class

Historical reviews of Song Gao: Reviewers describe the course as excellent and well-organized, offering a strong mix of technical geocomputing and real-world applications. Students consistently recommend it for those interested in GIS and data science, citing high quality and practical value.

Recent recorded grades — Spring 2023: 3.87 GPA, 93.5% A/AB (n=31 letter grades); Spring 2025: 3.90 GPA, 93.9% A/AB (n=49 letter grades); Spring 2026: 3.91 GPA, 100.0% A/AB (n=51 letter grades).

difficulty & workload

Historical reviews of Song Gao: The course is described as challenging, with one student noting it was among the most difficult they have taken. Preparation involves engaging with Python3, Jupyter Notebooks, Pandas, and GeoPandas for spatial analysis.

Historical reviews of Song Gao, Yuhao Kang: Students praise the responsible and helpful nature of the instructor and TA, who provide useful academic materials and patient support. The final project is highlighted as a great way to test newly learned skills.

Topics

  • Geospatial big data theory and components.
  • Geospatial big data storage, processing, and visualization.
  • AI and machine learning for geospatial problems.

Skills

  • Storing, processing, analyzing, and visualizing geospatial big data.
  • Applying AI and machine learning methods to geospatial problems.

Grades

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

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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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

1283 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 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
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": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "de5a963a87a0981f0846920ec61d2b1eeded2586bd3246fa8124b993f1f0ef58",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}