Advanced Geocomputing and Geospatial Big Data Analytics
Introduction to theory, techniques, and analytical methods for geospatial big data using advanced Python.
Summary
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
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course mapGEOG 378,COMP SCI 220, or graduate/professional standing
- GEOG 378
- COMPSCI 220
- graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- GEOG 378
- COMPSCI 220
- graduate/professional standing
Professors
Fall 2026No 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 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
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
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
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
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.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
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"
}