Fall 2026

Foundations of Statistical Learning for Business Analytics

An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.

offering recorded2–3 credits
Recorded instructors · Fall 2026 Kyohei Okumura

Summary

1 / 6

Historical reviews of Peng Shi: Attending lectures and paying attention covers all exam questions, as the professor asks them throughout the sessions.

Grade history

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

Prerequisites

Course map

GEN BUS 307,317704, 705, 881,ECON 400, 410,STAT/​MATH 310,STAT 333, 340, or declared in the Business Exchange program

“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

Fall 2026

Recent recorded grades — Fall 2025: 3.40 GPA, 58.2% A/AB (n=67 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Kyohei Okumura is the current instructor, but available reviews only cover historical instructor Peng Shi. Shi was praised as helpful, approachable, and an excellent lecturer who explains difficult material clearly. However, some students found his lectures occasionally boring.

KYOHEI OKUMURA is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

PENG SHI is recorded teaching in Fall 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. 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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 010Classroom Instruction25 / 350
LEC 011Classroom Instruction29 / 350

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

Meeting source records

Student experience

the class

Historical reviews for Peng Shi describe him as a helpful, approachable lecturer who explains difficult material easily, though some find his lectures boring.

Recent recorded grades — Spring 2025: 3.53 GPA, 73.7% A/AB (n=38 letter grades); Fall 2025: 3.72 GPA, 80.5% A/AB (n=149 letter grades); Spring 2026: 3.60 GPA, 90.0% A/AB (n=30 letter grades).

difficulty & workload

Historical reviews of Peng Shi: Attending lectures and paying attention covers all exam questions, as the professor asks them throughout the sessions.

Historical reviews of Peng Shi: R exercises mixed into lectures are useful for understanding the material, though the lecture style can be boring.

Topics

  • Feature selection and regularization methods.
  • Building predictive models.
  • Linear regression and classification models.
  • The bias-variance tradeoff.

Skills

  • Developing linear regression and classification models for prediction.
  • Applying feature selection, regularization, and managing the bias-variance tradeoff.

Grades

Latest available · Spring 2026— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
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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

1283 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.

Selected offering source records
GENBUS 656 · Fall 2026

Foundations of Statistical Learning for Business Analytics

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:231:025327",
    "course_id": "GENBUS 656",
    "course_uid": "course_874e30b1d4fa32884aa74250",
    "term_id": "1272",
    "source_course_id": "025327",
    "source_subject_id": "231",
    "title": "Foundations of Statistical Learning for Business Analytics",
    "credits_min": 2,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
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": "8f42c555a1b916d4226a5c5de489773aee150f0e186563733775902acd0eeac6",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}