Spring 2026 · Syllabus
Graduate · 3 credits · In person · First offering
Instructor & meeting information
- Instructor
- Yu Fu, Ph.D., Assistant Professor of Computer Science
- yu.fu@ucf.edu
- Office
- HEC 441
- Office hours
- Tuesday, Wednesday, and Thursday, 1–3 p.m.
- Class
- Fridays, 1:00–3:50 p.m.
- Location
- CB1 0112, Main Campus (Orlando)
Course description
Information Visualization focuses on the design of interactive visual systems that help people explore, analyze, and communicate data. Unlike scientific visualization, which often deals with inherently spatial data, information visualization addresses abstract, non-spatial datasets and emphasizes human perception, cognition, and interaction.
Drawing from computer science, human–computer interaction (HCI), design, psychology, and statistics, this course integrates research foundations with hands-on development. Students engage in seminar-style discussions of influential and current visualization research while building interactive web-based visualizations using D3.js, Vega-Lite, and modern web technologies. The course prepares students for both applied visualization practice and visualization/HCI research.
Prerequisites & preparation
Prior programming experience is recommended. Familiarity with JavaScript or web development is helpful but not required; students should be prepared for independent learning.
Learning objectives
- Explain core principles of information visualization, including perceptual, cognitive, and ethical foundations.
- Critically analyze and critique visualization designs and research literature with respect to effectiveness, interpretability, and communication goals.
- Design interactive visual encodings and interaction techniques grounded in visualization theory and empirical findings.
- Implement web-based visualizations and exploratory interfaces using D3.js, JavaScript, and modern web technologies.
- Identify open research problems and emerging opportunities in information visualization and HCI, and articulate informed design or research directions.
Course structure
Each class meeting integrates theory and practice through two coordinated components.
Lecture: Principles and Concepts
Lectures and guided discussions cover perceptual and cognitive foundations, visual encoding and interaction design, evaluation and critique of visualization systems, ethics and accessibility, and emerging research directions. Readings from classic and contemporary visualization research ground these discussions and expose students to open research opportunities.
Research & Lab: Paper Presentation + Programming and Implementation
Labs combine research engagement with hands-on coding. Students present and critique visualization research papers, then translate key ideas into practice by implementing visual encodings, interaction techniques, and narrative structures using D3.js, Vega-Lite, and modern web technologies. Lab activities are tightly aligned with lecture topics.
Assignments & grading
| Component | Weight | Description |
|---|---|---|
| Homework assignments (4) | 40% | Individual programming assignments |
| Reading presentation | 20% | Research paper presentation and discussion |
| Reading reflections | 10% | Written research critiques |
| Final visualization project | 20% | Culminating design and implementation project |
| Participation and engagement | 10% | Discussions, critiques, and labs |
Homework assignments
Four individual assignments that progress from introductory visualization tools to interactive, multi-view systems using Vega-Lite and D3.js.
Reading presentation
Each student will lead one in-class presentation (approximately 10 minutes) on an assigned research paper, followed by discussion.
Reading reflections
Short written critiques (2–3 pages) analyzing assigned research papers, focusing on design goals, methods, and contributions.
Final visualization project
A culminating project synthesizing visualization principles, interaction design, and implementation. Projects may include an exploratory visualization system, a narrative visualization, or a critical redesign of an existing visualization. Emphasis is placed on design rationale, grounding in visualization research, and technical execution.
Participation
Active participation in discussions, critiques, and labs is expected and contributes to the overall learning experience.
Weekly schedule — Spring 2026
Subject to adjustment based on class pace and interests.
| Week | Date (Friday) | Lecture: Concepts & principles | Research & lab | Homework / milestones |
|---|---|---|---|---|
| 1 | Jan 16 | Introduction to InfoVis | My path into VIS: a research talk | — |
| 2 | Jan 23 | Information visualization overview | Vega-Lite 1: basic specs & statistical charts | — |
| 3 | Jan 30 | Visual perception | Vega-Lite 2: layers & multiple views | — |
| 4 | Feb 6 | Multivariate representations: lines, axes, regions | Vega-Lite 3: maps & Altair | HW1 (Vega-Lite) assigned |
| 5 | Feb 13 | Multivariate representations: dots & glyphs | D3: introduction, resources, and web basics | — |
| 6 | Feb 20 | Graphical integrity | D3: HTML, CSS, SVG, JavaScript | HW2 (frontend development) assigned |
| 7 | Feb 27 | Interaction | D3: data loading and binding | — |
| — | Mar 6 | No class — conference travel | — | — |
| 8 | Mar 13 | Data humanism and storytelling | D3: scales, charts, and axes | — |
| 9 | Mar 20 | No class — spring break (Mar 16–21) | — | — |
| 10 | Mar 27 | Text & documents | D3: advanced charts | HW3 (D3 charts) assigned |
| 11 | Apr 3 | Visual analytics | D3: interaction I | — |
| 12 | Apr 10 | Time series data | D3: interaction II | HW4 (D3 interaction) assigned |
| 13 | Apr 17 | Hierarchies & trees; graphs & networks | D3: force layouts and network graphs | — |
| 14 | Apr 24 | Geospatial visualization | D3: scrollytelling | — |
| 15 | May 1 | Final project | Final project | Final project due |
Software, materials & readings
Required tools
- Modern web browser (Chrome or Firefox)
- Code editor (VS Code recommended)
- GitHub account
- Node.js for local development
Primary libraries
- D3.js
- Vega-Lite (introductory use)
Recommended readings
- Tamara Munzner — Visualization Analysis and Design
- Scott Murray — Interactive Data Visualization for the Web
- Colin Ware — Information Visualization: Perception for Design
- Edward Tufte — The Visual Display of Quantitative Information
Policies & support
Late policy
Assignments lose 10% per day, up to five days late. Each student may use up to two no-penalty late days during the semester. Late days do not apply to presentations or the final project.
Academic integrity
Students are expected to adhere to UCF’s standards of academic integrity. Plagiarism, unauthorized collaboration, or misrepresentation of work will result in disciplinary action in accordance with university policy.
Accessibility and support
Students requiring accommodations should contact Student Accessibility Services (SAS) and provide the instructor with the official accommodation letter as early as possible.
Additional university policies
This course follows all UCF policies regarding Title IX, campus safety, academic conduct, and financial aid accountability. Students are encouraged to review official UCF policy resources.