Spring 2027

Foundations of Statistical Learning for Business Analytics

Foundations of Statistical Learning for Business Analytics introduces predictive modeling for business, covering linear regression, classification, and the bias-variance tradeoff.

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

Summary

1 / 7

Past student reviews · Peng Shi

The course included practical R exercises mixed into lectures, which students described as useful for understanding the material despite some finding the lecture format occasionally dry.

What you’ll be able to do

  • Build (generalized) linear regression models for the purpose of prediction in business contexts, choosing from a variety of statistical learning techniques.

  • Select predictive algorithms and features for machine learning models in business settings, taking into account the fundamental bias-variance tradeoff.

  • Evaluate prediction accuracy of models for regression and classification problems for business applications.

  • Communicate the advantages and disadvantages of different prediction approaches, and justify and explain their choices in the context of business problems.

  • Interpret the prediction outcomes of a given model in a business context, and communicate the results to non-expert audiences.

Source: UW Guide

Grade history

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average GPA
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All recorded terms · compare terms & instructors

Prerequisites

Course map

GEN BUS 307, 317, 704, 705, 881,ECON 400, 410,STAT/​MATH 310, 410,STAT 333, 340, or declared in the Business Exchange program

GENBUS 656 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

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Spring 2027

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SectionModeEnrolled / capacityWaitlist

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Meeting source records
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Student experience

the class

Past student reviews · Peng Shi

Students found Peng Shi to be a highly effective lecturer who explained difficult coding and statistics material clearly, making the course approachable for those willing to engage with the content.

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

Past student reviews · Peng Shi

The course included practical R exercises mixed into lectures, which students described as useful for understanding the material despite some finding the lecture format occasionally dry.

Past student reviews · Peng Shi

Peng Shi was perceived as friendly and approachable, with students noting that attending lectures and paying attention covered the exam questions he posed throughout the sessions.

Topics

  • Linear regression and classification models for prediction.
  • Feature selection, regularization, and the bias-variance tradeoff.

Skills

  • Building generalized linear regression models for prediction using statistical learning techniques.
  • Selecting predictive algorithms and features while considering the bias-variance tradeoff.
  • Interpreting model prediction outcomes and communicating results to non-expert audiences.

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

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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.

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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.

Read original reviews
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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": "194ef7b59ba0ec7d2f7b2c12563a7aad1edab35ff146b56b0aec1aad99264801",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}