Spring 2027

Business Statistics Using Python

Business Statistics Using Python is a compact primer covering probability, estimation, inference, correlation, and regression for data-driven business analysis.

offering recorded2 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Spring 2025: 3.82 GPA, 88.1% A/AB (n=42 letter grades); Fall 2025: 3.92 GPA, 95.0% A/AB (n=40 letter grades); Spring 2026: 3.79 GPA, 95.2% A/AB (n=21 letter grades).

What you’ll be able to do

  • Calculate descriptive statistics and generate basic visualizations using Python.

  • Explain and apply principles and rules of probability.

  • Utilize inferential statistics and communicate the uncertainty in statistical estimates.

  • Perform regression analysis, and distinguish between correlation and causation.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing or declared in graduate Business Exchange program

    • Graduate/professional standing
    • declared in graduate Business Exchange program
    take one
GENBUS 881 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
  • Any of
    • Graduate/professional standing
    • declared in graduate Business Exchange program

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

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Meeting source records

No records available.

Student experience

the class

Recent recorded grades — Spring 2025: 3.82 GPA, 88.1% A/AB (n=42 letter grades); Fall 2025: 3.92 GPA, 95.0% A/AB (n=40 letter grades); Spring 2026: 3.79 GPA, 95.2% A/AB (n=21 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Probability, estimation, inference, correlation, and regression.
  • Descriptive statistics and basic visualizations using Python.

Skills

  • Calculating descriptive statistics and generating basic visualizations using Python.
  • Explaining and applying principles and rules of probability.
  • Utilizing inferential statistics and communicating uncertainty in statistical estimates.
  • Performing regression analysis and distinguishing between correlation and causation.

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

Not enough comparable courses for Spring 2027 in UW–Madison.

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
GENBUS 881 · Spring 2027

Business Statistics Using Python

Recorded 2026-10-14
Raw records 
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  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:231:026420",
    "course_id": "GENBUS 881",
    "course_uid": "course_49f49c69936bd20b3d9b122c",
    "term_id": "1274",
    "source_course_id": "026420",
    "source_subject_id": "231",
    "title": "Business Statistics Using Python",
    "credits_min": 2,
    "credits_max": 2,
    "typically_offered": "Not Applicable"
  }
]
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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
Download part 1
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": "fb3b23243a62dedd3e38d7d2d2d519f7820894604144f934cf870814646cbfcb",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}