Spring 2027

Prescriptive Modeling and Optimization for Business Analytics

Introduction to prescriptive analytics and business optimization using Excel and Python, covering model design, testing, and solving linear, integer, and nonlinear problems.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2023: 3.62 GPA, 83.0% A/AB (n=112 letter grades); Fall 2024: 3.81 GPA, 91.2% A/AB (n=148 letter grades); Fall 2025: 3.85 GPA, 95.2% A/AB (n=146 letter grades).

What you’ll be able to do

  • Identify appropriate modeling, analysis, and optimization methodologies when faced with a concrete problem.

  • Leverage spreadsheet software (e.g., Excel) and programming languages (e.g., Python) for modeling and optimization.

  • Classify optimization problems (e.g., linear, integer, nonlinear) and explain the challenges of solving different problem types.

  • Recognize classic optimization problems (e.g., knapsack, network, traveling salesman) and recall how to formulate them.

  • Discuss fundamental optimization theoretical concepts and their practical implications.

Source: UW Guide

Grade history

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

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
GENBUS 730

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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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
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Student experience

the class

Recent recorded grades — Fall 2023: 3.62 GPA, 83.0% A/AB (n=112 letter grades); Fall 2024: 3.81 GPA, 91.2% A/AB (n=148 letter grades); Fall 2025: 3.85 GPA, 95.2% A/AB (n=146 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Fundamentals of prescriptive analytics for business
  • Mathematical optimization with Excel and Python
  • Model design, testing, sensitivity, and risk analysis
  • Linear, integer, and nonlinear optimization models

Skills

  • Designing, building, testing, and analyzing models with sensitivity and risk analysis
  • Developing and solving linear, integer, and nonlinear optimization models
  • Using Excel and Python for modeling and optimization
  • Classifying optimization problems and explaining solution challenges

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1316 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 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
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": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "b3b0d6a1fa9d149e7317875b5cb382831c44a367a19cc64683ecb23342ec2d62",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}