Spring 2027

Data Storytelling with Visualization

Introduction to data visualization covering effective tools for summarizing and analyzing data, limitations, and planning visualizations using software platforms.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Xiang Zheng

Students noted that understanding the concepts required some effort, but quizzes and assignments were easy if you attended class and followed the demonstrations.

What you’ll be able to do

  • Understand theories of data visualization

  • Explain how and why visualization can be an effective tool for summarizing, analyzing and communicating about data

  • Understand and apply best practices for communicating about data for targeted and broad audiences

  • Understand and apply theories and best practices for creating visualizations

  • Design and develop informative and persuasive visualizations

  • Identify and choose appropriate software for creating visualizations

  • Choose appropriate visualization types based on source data, audience, and goal

  • Critically analyze visualizations created by others for effectiveness and bias

  • Understand the limitations and challenges of data visualization

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

Sophomore standing and satisfied Quantitative Reasoning (QR) A

    • Sophomore standing
    • satisfied Quantitative Reasoning (QR) A
    take all
LIS 407

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

Prerequisite text tree
  • All of
    • Sophomore standing
    • satisfied Quantitative Reasoning (QR) A

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

JEFF KRITZMAN is recorded teaching in Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

XIANG ZHENG is recorded teaching in 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
LEC 001Classroom Instruction0 / 1200

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

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Jeffrey Kritzman

Jeffrey Kritzman delivered engaging lectures and unique, fun projects, though assigned readings were somewhat repetitive.

Recent recorded grades — Spring 2025: 3.64 GPA, 82.0% A/AB (n=128 letter grades); Fall 2025: 3.57 GPA, 82.1% A/AB (n=156 letter grades); Spring 2026: 3.43 GPA, 73.6% A/AB (n=121 letter grades).

difficulty & workload

Past student reviews · Xiang Zheng

Students noted that understanding the concepts required some effort, but quizzes and assignments were easy if you attended class and followed the demonstrations.

Past student reviews · Xiang Zheng

Xiang Zheng was available after class to help students and answer questions, contributing to a supportive learning environment.

Topics

  • Limitations and challenges in using data visualizations, including misrepresentation and bias
  • Planning appropriate types of visualization based on source data, audience, and goals
  • Hands-on experience with popular visualization software platforms

Skills

  • Identify and choose appropriate software for creating visualizations
  • Choose appropriate visualization types based on source data, audience, and goal
  • Critically analyze visualizations created by others for effectiveness and bias
  • Design and develop informative and persuasive visualizations

Grades

Latest available · Spring 2026— 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 Spring 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

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.

Download part 1
Selected offering source records
LIS 407 · Spring 2027

Data Storytelling with Visualization

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": "1274:544:026072",
    "course_id": "LIS 407",
    "course_uid": "course_26a3253bb559893e7f2e983f",
    "term_id": "1274",
    "source_course_id": "026072",
    "source_subject_id": "544",
    "title": "Data Storytelling with Visualization",
    "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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "99311faa4f9577d1ebd0862f352723d32558131e80371c66d9933dfee39568b9",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}