Spring 2027

Introduction to Data Visualization

Introduction to Data Visualization surveys perception, cognition, and design fundamentals, offering hands-on activities to gain real-world experience in designing and evaluating visualizations.

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

Summary

1 / 6

Past student reviews · Cynthia Jasper

Students led two discussions and wrote one 8-10 page paper that was graded solely on completion.

What you’ll be able to do

  • Differentiate between visualizations to communicate within situational context.

  • Illustrate the types of properties that make visualizations more effective.

  • Apply techniques and algorithms for creating effective visualizations based on principles and techniques from graphic design, visual art, perceptual psychology, and cognitive science.

  • Utilize visualization creation tools in individual and group assignment

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

COMP SCI 320, 400, or Graduate/Professional Standing

COMPSCI 565

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

Prerequisite text tree

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

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

No records available.

Student experience

the class

Past student reviews · Cynthia Jasper

A former student described the course as very easy and highly enjoyable, noting that the instructor was a favorite professor.

Recent recorded grades — Spring 2024: 3.84 GPA, 92.1% A/AB (n=76 letter grades).

difficulty & workload

Past student reviews · Cynthia Jasper

Students led two discussions and wrote one 8-10 page paper that was graded solely on completion.

Past student reviews · Cynthia Jasper

The reviewer found the class to be a favorite experience, citing the instructor's awesome teaching style.

Topics

  • Perception, cognition, communication, design, implementation, applications, tools, and evaluation.
  • Fundamental concepts, theory, and tools in data visualization.
  • Principles and techniques from graphic design, visual art, perceptual psychology, and cognitive science.

Skills

  • Differentiating between visualizations for communication within situational contexts.
  • Illustrating properties that enhance visualization effectiveness.
  • Applying techniques and algorithms from graphic design, visual art, perceptual psychology, and cognitive science to create effective visualizations.
  • Utilizing visualization creation tools in individual and group assignments.

Grades

Latest available · Spring 2024— 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
More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2024 · 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

1238 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.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "30189afba0951debf565a14fd07f77c71c24b757e473e02e83376c1f7408de71",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}