Spring 2027

Business Analytics I

Business Analytics I develops quantitative intuition via database management, visualization, probability, and regression analysis using Excel and business cases.

offering recorded3 credits
Recorded instructors · Spring 2027 Richard Crabb4.3/5

Summary

1 / 6

Past student reviews · Richard Crabb

Students described the exam as long and difficult, while case studies required significant time. One student noted the absence of graded homework, quizzes, or a textbook in their term.

What you’ll be able to do

  • Develop “statistical literacy,” meaning that you can interpret statistics frequently used in current events, industry reports, and so on

  • Distinguish between descriptive and inferential statistics, and apply skills such as data summarization, hypothesis testing, and regression analysis, using Excel

  • Apply the core concepts of probability to decision-making under uncertainty, including an introduction to simulation

  • Synthesize your knowledge with quantitative business cases

  • Create effective communication of data analyses in written, visual, and/or oral formats

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(GEN BUS 106 or concurrent enrollment) and (MATH 211, 217, or 221), or declared in undergraduate Business Exchange program

GENBUS 306 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

Richard Crabb

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.51/5 from 69 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.

For this course: 4.5/5 raw quality · 3.1/5 difficulty · 31 reviews

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

Past student reviews · Richard Crabb

Richard Crabb taught the course using lecture videos and Excel case studies, with some students reporting no textbook or graded homework. While the exam was described as hard and long, others noted it was manageable with practice problems and preparation. Students generally found Crabb engaging and funny, though some found the lectures difficult to follow.

Recent recorded grades — Spring 2025: 3.27 GPA, 51.2% A/AB (n=170 letter grades); Fall 2025: 3.31 GPA, 57.0% A/AB (n=435 letter grades); Spring 2026: 3.25 GPA, 47.7% A/AB (n=478 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Past student reviews · Anita Mukherjee, Hessam Bavafa

Past students described the course as including group Excel cases and a short answer exam. Anita Mukherjee was noted for clear directions and helpful videos, though one student found the projects annoying. Hessam Bavafa delivered engaging lectures without a textbook, but students emphasized attending class and starting case writeups early because lecture slides alone offered limited detail.

ANITA MUKHERJEE is recorded teaching in Fall 2016, Fall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Spring 2023. Recorded history may be incomplete and does not establish a future schedule.

HESSAM BAVAFA is recorded teaching in Fall 2016, Fall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2022, Spring 2023, Fall 2023, Spring 2025, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

RICHARD CRABB is recorded teaching in Fall 2016, Spring 2017, Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 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

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
DIS 301Classroom Instruction0 / 400
DIS 302Classroom Instruction0 / 400
DIS 303Classroom Instruction0 / 400
DIS 304Classroom Instruction0 / 450
DIS 305Classroom Instruction0 / 450
DIS 306Classroom Instruction0 / 450
DIS 307Classroom Instruction0 / 450
DIS 308Classroom Instruction0 / 450
DIS 309Classroom Instruction0 / 450
LEC 002Classroom Instruction0 / 1350
LEC 003Classroom Instruction0 / 1350
LEC 004Classroom Instruction0 / 2400
DIS 310Classroom Instruction0 / 400
DIS 311Classroom Instruction0 / 400
DIS 312Classroom Instruction0 / 400

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

Meeting source records
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Student experience

the class

Past student reviews · Richard Crabb

Richard Crabb delivered engaging lectures using real-world applications and humor, which students found helpful for making the challenging material more accessible and memorable.

Recent recorded grades — Spring 2025: 3.27 GPA, 52.7% A/AB (n=484 letter grades); Fall 2025: 3.31 GPA, 57.0% A/AB (n=435 letter grades); Spring 2026: 3.25 GPA, 47.7% A/AB (n=478 letter grades).

difficulty & workload

Past student reviews · Richard Crabb

Students described the exam as long and difficult, while case studies required significant time. One student noted the absence of graded homework, quizzes, or a textbook in their term.

Past student reviews · Richard Crabb

Students found practice problems helpful for surviving exams, and one student noted that TA grading on case write-ups was a bit subjective.

Topics

  • Database management, summarization, exploration, and visualization
  • Probability and decision-making under uncertainty
  • Hypothesis testing, regression analysis, and simulation methods
  • Big data and data mining
  • Descriptive and inferential statistics

Skills

  • Managing, summarizing, exploring, and visualizing databases
  • Data summarization, hypothesis testing, and regression analysis using Excel
  • Applying probability concepts to decision-making under uncertainty and simulation
  • Effective communication of data analyses in written, visual, and oral formats
  • Interpreting statistics used in current events and industry reports

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

8 earlier forecasts · 0.08 GPA average error.

Grades over time

Through Spring 2027

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
GENBUS 306 · Spring 2027

Business Analytics I

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": "1274:231:024534",
    "course_id": "GENBUS 306",
    "course_uid": "course_801a8dc807b21e1c6c93cc3f",
    "term_id": "1274",
    "source_course_id": "024534",
    "source_subject_id": "231",
    "title": "Business Analytics I",
    "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
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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 1Download part 2
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": "0fb005867cf3c313d960808258e0c64f1999cb5bf3f4056cf31174c80a0c1216",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}