Programming for Earth Scientists
Programming for Earth Scientists introduces scientific programming in Python for geoscience applications, covering numerical computing, machine learning, and data visualization.
Summary
No student feedback recorded yet.
Grade history
↗letter grades
No recorded grade history.
All recorded terms · compare terms & instructors
Prerequisites
Course mapProfessors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
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
Introduction to scientific programming with a focus on geoscience applications, utilizing Python as the primary programming language. Focus on gaining practical skills applicable to geoscience datasets such as analytical model implementation, time series analysis, and geospatial data visualization. Includes elementary topics in numerical computing and machine learning. The use of hands-on exercises, real-world datasets, and collaborative projects will be used to explore how to address geoscience problems with computational solutions.
difficulty & workload
No workload feedback recorded.
Topics
Skills
Grades
Grade distribution · % of letter grades
No grades available yet.
More grade details Grade mix, volume & source data
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
No records available.
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": "002f6fb7d8b9635843fb07c4dc390015d2d8cfee4b0d2b13ab2ef4ec90db2813",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}