Spring 2027

Mathematical Foundations of Business Analytics

Calculus-based introduction to statistical inference and modeling (regression, time series) applied to business data for decision-making under uncertainty.

offering recorded3 credits
Recorded instructors · Spring 2027 Paul Johnson3.7/5

Summary

1 / 3

Recent recorded grades — Spring 2025: 3.41 GPA, 59.4% A/AB (n=32 letter grades); Fall 2025: 3.41 GPA, 68.6% A/AB (n=35 letter grades); Spring 2026: 3.49 GPA, 75.7% A/AB (n=37 letter grades).

What you’ll be able to do

  • Apply discrete and continuous probability distributions to data encountered in business applications and evaluate the quality of parameter estimates.

  • Explain the basics of modeling using both regression and time series approaches.

  • Recognize the assumptions and limitations of the methods utilized in this course to aid businesses in making decisions under uncertainty.

  • Interpret statistical information for someone with a non/less technical background.

  • Analyze business data and construct models using appropriate statistical software.

  • Communicate and present the results of an analysis.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 331,STAT/​MATH 309, or 431

GENBUS 317 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Paul Johnson

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.80/5 from 15 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

Recent recorded grades — Spring 2025: 3.41 GPA, 59.4% A/AB (n=32 letter grades).

Historical instructors & teaching patterns

PAUL JOHNSON is recorded teaching in Spring 2025. 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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 550
LAB 301Classroom Instruction0 / 550

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Recent recorded grades — Spring 2025: 3.41 GPA, 59.4% A/AB (n=32 letter grades); Fall 2025: 3.41 GPA, 68.6% A/AB (n=35 letter grades); Spring 2026: 3.49 GPA, 75.7% A/AB (n=37 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Point estimation, confidence intervals, and hypothesis testing
  • Regression and time series models for business analytics
  • Quantifying uncertainty in parameter estimates

Skills

  • Apply probability distributions to business data
  • Model using regression and time series approaches
  • Construct models using statistical software

Grades

Latest available · Spring 2026— 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 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

1280 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.

Download part 1
Selected offering source records
GENBUS 317 · Spring 2027

Mathematical Foundations of Business Analytics

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:231:026584",
    "course_id": "GENBUS 317",
    "course_uid": "course_3f32a30c0076803c25219bbc",
    "term_id": "1274",
    "source_course_id": "026584",
    "source_subject_id": "231",
    "title": "Mathematical Foundations of Business Analytics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "862cf2120441ad32f34530a066bafb93f97acc9f912c1e79c860f42cc91923ab",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}