Spring 2027

Data Visualization

Data Visualization covers principles of visual data presentation, surveying Information Visualization, Scientific Visualization, and Visual Analytics, with focus on designing and evaluating visualizations and interactive tools.

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

Summary

1 / 6

Past student reviews · Michael Gleicher

One student reported that despite significant effort on homework and projects, the final grading process remained opaque and unstructured.

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI 765

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

MICHAEL GLEICHER is recorded teaching in Spring 2017, Fall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2024, Fall 2025. 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

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

Meeting source records
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Student experience

the class

Past student reviews · Michael Gleicher

Past students described the course as unorganized, with unclear expectations and grading for most of the semester.

Recent recorded grades — Fall 2022: 3.62 GPA, 90.0% A/AB (n=60 letter grades); Fall 2024: 3.60 GPA, 92.5% A/AB (n=67 letter grades); Fall 2025: 3.66 GPA, 95.5% A/AB (n=44 letter grades).

difficulty & workload

Past student reviews · Michael Gleicher

One student reported that despite significant effort on homework and projects, the final grading process remained opaque and unstructured.

Past student reviews · Michael Gleicher

A recent review characterized the instructor as extremely unorganized and stated that the professor singled out students.

Topics

  • Information Visualization, Scientific Visualization, and Visual Analytics
  • Foundations in visual design, human perception, data analysis, encodings, layout, and interaction
  • Visualization of complex data types including scalar fields, graphs, sets, texts, and multivariate data
  • Use of 2D, 3D, and motion in data presentations and implementation issues

Skills

  • Designing and evaluating visualizations and interactive exploration tools
  • Applying approaches to visualize large datasets and complex data types like scalar fields, graphs, sets, texts, and multivariate data
  • Using 2D, 3D, and motion in data presentations and addressing implementation issues

Grades

Latest available · Fall 2025— 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 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1316 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 
[]
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
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "0113fd84624d7ebaac0c13c761fc4e0745ad515951cb3ede8b375b20eedd66d3",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}