Fall 2026

Consumer Analytics

Consumer Analytics teaches leveraging data analysis to drive business decisions through evidence-based storytelling.

offering recorded3 credits
Recorded instructors · Fall 2026 Megan BeaWanting Jiang

Summary

1 / 6

Exams are hard, but students learn a lot from them according to historical reviews of Yiwei Zhang.

Grade history

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

Prerequisites

Course map

CNSR SCI 201

CNSRSCI 301

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026

Recent recorded grades — Fall 2022: 3.41 GPA, 79.5% A/AB (n=44 letter grades); Spring 2023: 3.55 GPA, 81.5% A/AB (n=54 letter grades); Spring 2024: 3.15 GPA, 65.2% A/AB (n=23 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Historical reviews for Yiwei Zhang praise her for making material easier to understand and providing extensive resources like videos and PowerPoint slides. Reviewers also note that her exams are hard but effective for learning.

MEGAN BEA is recorded teaching in Spring 2020, Spring 2022, Fall 2022, Spring 2023, Spring 2024. Recorded history may be incomplete and does not establish a future schedule.

YIWEI ZHANG is recorded teaching in Fall 2019, Fall 2020, Fall 2021, Fall 2023, 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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction48 / 480

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

Historical reviews of Yiwei Zhang: Yiwei Zhang teaches statistics effectively, making the class great for analytics enthusiasts despite hard exams and early 8 am sections.

Recent recorded grades — Spring 2025: 3.42 GPA, 67.2% A/AB (n=61 letter grades); Fall 2025: 3.47 GPA, 68.6% A/AB (n=70 letter grades); Spring 2026: 3.16 GPA, 59.2% A/AB (n=49 letter grades).

difficulty & workload

Exams are hard, but students learn a lot from them according to historical reviews of Yiwei Zhang.

Historical reviews of Yiwei Zhang: Yiwei Zhang makes material easier to understand and provides extensive outside resources like videos and powerpoints, with constructive feedback on projects.

Topics

  • Data analysis process for business actions
  • Transforming analytical results into stories

Skills

  • Leverage data analysis for business decisions
  • Tell compelling evidence-based stories
  • Execute data analysis processes
  • Transform analytical results into effective stories

Grades

Historical instructor

Fall 2026 · 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 4 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.

5 earlier forecasts · 0.10 GPA average error.

Grades over time

Through Fall 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

1283 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.

Download part 1
Selected offering source records
CNSRSCI 301 · Fall 2026

Consumer Analytics

Recorded 2026-09-07
Raw records 
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:271:024473",
    "course_id": "CNSRSCI 301",
    "course_uid": "course_e420cb4ad0fb5cdf16dee294",
    "term_id": "1272",
    "source_course_id": "024473",
    "source_subject_id": "271",
    "title": "Consumer Analytics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
  }
]
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 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "e4355db7636ef08f4684d08452e605c0d4501e494e63d2b9ff11ab5548109598",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}