INST462

Introduction to
Data Visualization


Instructor: Keke Wu
Semester: Spring 2026
Time: Tue 11–12:15 EST (Lecture) | Fri 9:30–1:45 EST (Discussion)

Image credit: beautiful.ai

Menu

Absent?

If you will miss a lecture, assignment, and/or discussion, please submit the absence form: Absence Form

Course Description

This course explores how data becomes useful through visualization by integrating design, storytelling, accessibility, and interaction. Students learn to transform raw information into clear, compelling visual and spatial experiences while considering how people perceive, interpret, and engage with data across digital, physical, multimodal, and immersive formats. Through lectures, hands-on exercises, and three creative projects, students practice visual encoding, critique, narrative design, and ethical reasoning, creating visualizations that are accurate, inclusive, emotionally resonant, and human-centered, while developing the ability to communicate insights effectively and evaluate their impact on diverse audiences.


We meet twice per week in a lecture–studio format. Tuesday sessions introduce key data visualization concepts through lectures and discussion. Friday sessions are studio- and lab-based, where students work in small groups on hands-on activities and project work, applying concepts from earlier in the week through experimentation, critique, and iteration.

Learning Objectives

By the end of this course, students will:

  1. Design clear, effective visualizations grounded in visual encoding and perception.
  2. Craft meaningful data stories through narrative structure, framing, and annotation.
  3. Create accessible and inclusive data experiences, including physical, sensory, and spatial forms.
  4. Build visualizations with multiple tools and select methods suited to different audiences and goals.
  5. Evaluate and communicate visualization work with attention to ethics, audience, and design impact.

Teaching Team

Instructor

Assistant Professor @ INFO

Office Hours: By appointment (here)

Teaching Assistant: Jay Patel (Ph.D. Candidate, INFO) (he/his/him)

Email: ppatel45@umd.edu

Office Hours: Tue, 1:00–4:45 PM (Zoom)

Graduate Course Aide: Nisank Arunkumar

Email: narnav1@umd.edu

Undergraduate Course Aide: Giulia Hoorens van Heyningen (She/Her)

Email: ghvh@terpmail.umd.edu

Undergraduate Course Aide: Ali Beshir (He/Him)

Email: abeshir@terpmail.umd.edu

Schedule

We meet twice a week on Tuesdays and Fridays. Tuesday sessions introduce core concepts through lecture, discussion, and activities. Friday sessions are hands-on studio meetings where you practice, prototype, and apply the week’s ideas through guided activities or project work. Items marked with a gray square indicate projects.

Week Topic Tuesday Friday Assignment
1 Introduction 1/27 1/30 Data Selfie
2 Visual Perception 2/3 2/6 Good / Bad Design
3 Data & Encodings 2/10 2/13 Colorful Data
4 Design Principles 2/17 2/20 P1: Data-Driven Vlog
5 Tools & Techniques 2/24 2/27 P1 Due
6 Data Storytelling 3/3 3/6 Data Comic
7 Affect & Aesthetics 3/10 3/13 Affective Data
8 Spring Break 🏖 🏖 🏖🏖🏖
9 Ethics & Activism 3/24 3/27 P2: Data-Driven Poster
10 Inclusive Visualization 3/31 4/3 P2
11 Immsersive Visualization 4/7 4/10 P2 Due
12 Evaluation 4/14 4/17 P3 Data-Driven Narrative
13 Storyboarding 4/21 4/24 P3.1 - Storyboard
14 Design Lab 4/28 5/1 P3.2 - Contribution Statement
15 Final Presentation 5/5 5/8 P3.3 - Final Materials & Presentation