Hydrologic Data Analysis
Introduces probability and statistics for analyzing and modeling real-world problems in hydrology, water resources engineering, and environmental data analysis.
Summary
Recent recorded grades — Fall 2024: 3.65 GPA, 84.6% A/AB (n=13 letter grades).
What you’ll be able to do
Demonstrate data literacy in the context of temporal and geospatial hydrologic and environmental datasets
Employ modern statistical software to analyze and model hydrologic and other environmental phenomena
Detect trends, relationships, and patterns in temporal and spatial hydrologic and environmental datasets using regression, extreme value analyses, and other popular hydrologic statistical techniques
Derive and interpret predictions, parameters, confidence levels, uncertainties, and sensitivities in the context of common hydrologic applications such as rainfall/flood frequency, hydropower, streamflow forecasting, and water quality
Identify applications of data analysis methods to specific water resources engineering and other civil and environmental engineering applications
Identify and apply appropriate statistical analysis methods to novel environmental datasets and problems
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(MATH 221 or 217) and (STAT 311, 324, 340,MATH/STAT 309, 409, 431,B M E 325,I SY E 210, orE C E 331), graduate/professional standing, or member of Engineering Guest Students
- MATH 221
- 217
- take one
- graduate/professional standing
- member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- All of
- graduate/professional standing
- member of Engineering Guest Students
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule 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
Recent recorded grades — Fall 2024: 3.65 GPA, 84.6% A/AB (n=13 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Probability and statistics applied to hydrology, water resources engineering, and environmental data analysis.
- Regression, extreme value analyses, and other hydrologic statistical techniques for detecting trends and patterns.
- Hydrologic applications including rainfall/flood frequency, hydropower, streamflow forecasting, and water quality.
Skills
- Probability and statistics for analyzing and modeling real-world problems in hydrology, water resources engineering, and environmental data analysis.
- Using modern statistical software to analyze and model hydrologic and environmental phenomena.
- Deriving and interpreting predictions, parameters, confidence levels, uncertainties, and sensitivities for hydrologic applications like flood frequency and streamflow forecasting.
Grades
Latest available · Fall 2024— not enough history to project Spring 2027.
Grade distribution · % of letter grades
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
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
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "5f4b44e310564758bdb10b6e2a6f2aa384dec80435df7476ea4f3ba5191c1704",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}