Spring 2027

Business Analytics II

Business Analytics II focuses on predictive and prescriptive analytics, using historical data for forecasting and decision models to identify optimal managerial actions.

offering recorded3 credits
Recorded instructors · Spring 2027 Michael Hernke4.2/5Kyeongbin Kim

Summary

1 / 6

Past student reviews · Michael Hernke

Students emphasized the need to practice Excel skills before exams. One review noted that earning every point outside the final was necessary to have a good chance at an A.

What you’ll be able to do

  • Explain the principles of different regression techniques for different forms of data

  • Discriminate between types of explanatory variables and ways to incorporate these variables in a statistical model

  • Implement and interpret output from the most common statistical methodologies

  • Recognize the structure of relational databases

  • Construct basic optimization and simulation models

  • Understand the collection, management, reasoning, and communication with data in corporate and other environments

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

GEN BUS 106 and 306, or declared in undergraduate Business Exchange program

GENBUS 307 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

Michael Hernke

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.44/5 from 41 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: 4.0/5 raw quality · 3.3/5 difficulty · 7 reviews

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

Past student reviews · Michael Hernke

Michael Hernke taught this class with a mix of positive and critical feedback. Students found him patient and accommodating, with one noting he reached out for exam extensions, while another described his lectures as dull and boring. Reviewers noted his teaching style was engaging and he was very helpful during practice days.

Recent recorded grades — Spring 2025: 3.37 GPA, 69.0% A/AB (n=287 letter grades); Fall 2025: 3.23 GPA, 62.3% A/AB (n=451 letter grades); Spring 2026: 3.27 GPA, 64.6% A/AB (n=206 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Past student reviews · Paul Hoban, Remi Daviet

Students reported that Paul Hoban's interactive lectures included weekly computer lab activities and difficult exams, with one noting the midterm was a time crunch. Remi Daviet received mixed feedback; some students found him knowledgeable and helpful, while others described his grading as aggressive and his exams as tedious, noting that attendance significantly impacted performance.

MICHAEL HERNKE 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.

PAUL HOBAN is recorded teaching in Spring 2017, Spring 2018, Spring 2019, Spring 2020. Recorded history may be incomplete and does not establish a future schedule.

REMI DAVIET is recorded teaching in 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

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 1040
DIS 301Classroom Instruction0 / 1040
LEC 002Classroom Instruction0 / 1040
DIS 302Classroom Instruction0 / 1040
LEC 003Classroom Instruction0 / 1040
DIS 303Classroom Instruction0 / 1040
LEC 004Classroom Instruction0 / 1040
DIS 304Classroom Instruction0 / 1040
LEC 005Classroom Instruction0 / 600
DIS 305Classroom Instruction0 / 600

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 · Michael Hernke

Michael Hernke used an engaging, story-based teaching style that students found interesting. He was described as passionate about the material and patient during practice days.

Recent recorded grades — Spring 2025: 3.31 GPA, 64.2% A/AB (n=425 letter grades); Fall 2025: 3.23 GPA, 62.3% A/AB (n=451 letter grades); Spring 2026: 3.22 GPA, 58.9% A/AB (n=419 letter grades).

difficulty & workload

Past student reviews · Michael Hernke

Students emphasized the need to practice Excel skills before exams. One review noted that earning every point outside the final was necessary to have a good chance at an A.

Past student reviews · Michael Hernke

One student appreciated Hernke's understanding, noting he offered an exam extension during personal struggles. Conversely, another student felt their grade was unfair despite working hard in the class.

Topics

  • Predictive analytics and forecasting
  • Prescriptive analytics and decision modeling
  • Regression techniques
  • Relational database structure
  • Optimization and simulation modeling

Skills

  • Explain principles of regression techniques for different data forms
  • Discriminate between explanatory variable types and their incorporation into statistical models
  • Implement and interpret output from common statistical methodologies
  • Recognize the structure of relational databases
  • Construct basic optimization and simulation models
  • Understand data collection, management, reasoning, and communication in corporate environments

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 · 0.14 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 307 · Spring 2027

Business Analytics II

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": "1274:231:024535",
    "course_id": "GENBUS 307",
    "course_uid": "course_412bfa78215919930228ef5e",
    "term_id": "1274",
    "source_course_id": "024535",
    "source_subject_id": "231",
    "title": "Business Analytics II",
    "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
Download part 1
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 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "abaa3c03c0e382987a05085e3658fc6d8bba4cfe0e5f55830aeae0bd7847e0a9",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}