Spring 2027

Statistics and Programming for Business Analytics

Statistics and Programming for Business Analytics provides a primer in statistics and programming for data-driven business analyses.

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

Summary

1 / 3

Recent recorded grades — Fall 2019: 3.85 GPA, 100.0% A/AB (n=23 letter grades); Fall 2020: 3.63 GPA, 73.2% A/AB (n=41 letter grades).

What you’ll be able to do

  • Derive measures related to the characteristics of probability functions such as moments, mean, variance, median,mode, and percentiles.

  • Carry out an ordinary least-squares (OLS) regression and interpret the parameter estimates as well as predictions.

  • Prepare, compile, and run basic computer programs in a variety of programming languages including C/C++,Java, Python, R, Stata, and VBA.

  • Implement algorithms for business analytics problems in a structured fashion in Python and R.

  • Pick suitable numerical functions from available packages, including quadrature (numerical integration), numerical solution of non-linear equations, optimization, and random-number generation, and use the functionality for business analyses.

  • Run a Monte Carlo simulation using R and Python.

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 705 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
  • 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

No records available.

Student experience

the class

Recent recorded grades — Fall 2019: 3.85 GPA, 100.0% A/AB (n=23 letter grades); Fall 2020: 3.63 GPA, 73.2% A/AB (n=41 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Random variables, probability distributions, estimation, and OLS regression.
  • Python and R programming with numerical and statistical packages.

Skills

  • Elementary statistics including random variables, probability distributions, estimation, and OLS regression.
  • Programming in Python and R using numerical and statistical packages for business applications.
  • Running Monte Carlo simulations in R and Python.

Grades

Latest available · Fall 2020— 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 2020

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2020 · 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

1108 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": "8ef5f0f0c42934ccfe2d4c7885cbb366b110f7882b8113895b8416545590b918",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}