Risk Analytics
Risk Analytics develops tools for modeling and managing business risk using probabilistic forecasting, integrating machine learning with distribution-based regression.
Summary
Recent recorded grades — Spring 2024: 3.39 GPA, 60.9% A/AB (n=23 letter grades); Spring 2025: 3.60 GPA, 73.5% A/AB (n=34 letter grades); Spring 2026: 3.34 GPA, 59.1% A/AB (n=22 letter grades).
What you’ll be able to do
Articulate the difference between point prediction and probabilistic prediction in a business context.
Build predictive models for the purpose of probabilistic forecasting in business applications.
Select appropriate modeling strategy and evaluate performance of the models in a given business context.
Perform feature engineering to incorporate complex pattern and unstructured data into the predictive model development for business applications.
Interpret the prediction outcome and communicate results to non-technical business audiences
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(ACT SCI 640,GEN BUS 656,STAT 333, or 340), or declared in undergraduate Business Exchange program
- take one
- declared in undergraduate Business Exchange program
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- Any of
- declared in undergraduate Business Exchange program
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 — Spring 2024: 3.39 GPA, 60.9% A/AB (n=23 letter grades); Spring 2025: 3.60 GPA, 73.5% A/AB (n=34 letter grades); Spring 2026: 3.34 GPA, 59.1% A/AB (n=22 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Heavy-tailed regression, count data regression, and survival data analysis
- Feature engineering using neural networks and natural language processing
- Probabilistic forecasting notation
Skills
- Modeling, communicating, and managing risk and uncertainty
- Building predictive models for probabilistic forecasting
- Feature engineering for complex patterns and unstructured data
- Interpreting predictions and communicating results to non-technical audiences
Grades
Latest available · Spring 2026— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2026
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
No records available.
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": "484193b71e97ca3ea0cd0cab2d86eb029ca459e6a9636041ff38529b519c8cbe",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}