Course Syllabus

Course Description

This course is designed to provide students an introduction to, and practical experience with, designing and building interactive information visualizations. Students will learn to create effective visual representations of structured data like numbers or text using visual encodings like position, size, hue, and value. Visualizations are used to amplify human perceivers’ abilities to identify important insights in data in a number of domains. Being able to design and implement visual representations and interactions using available toolkits provides students useful experience for jobs in data-analysis, communication, and software engineering.

The course provides students with the knowledge and skills needed to use empirically-based principles and rankings for choosing effective visual encodings for data. The content students will be exposed to includes data and image models, perception, exploratory data analysis, interaction, text visualization, and graph visualization, among other topics.  Students will also receive exposure to exploratory data analysis software and software toolkits for visualization.

As part of this course, you will complete a hands-on project that guides you through the full visualization process—from modeling data to building an interactive visualization. This project will challenge you to define a guiding question, formulate hypotheses, surface meaningful stories from the data, and iterate on your design by exploring and refining different visualization strategies.

Prerequisites

INFO 340 or CSE 154; CSE 123, 143, or CSE 163; and either QMETH 201, Q SCI 381, STAT 220, STAT 221/CS&SS 221/SOC 221, STAT 290, STAT 311, or STAT 390.

Student Learning Outcomes

By the end of this course, students will be able to:

1. Design effective data visualizations by applying empirically-supported principles of visual encoding and tailoring designs to specific data types, user tasks, and context.
2. Use existing visualization tools (e.g., Tableau and Rowboat) to explore, manipulate, and present structured datasets, identifying key patterns and trends.
3. Develop interactive data visualizations using web-based technologies (e.g., P5.JS), incorporating user-driven features such as real-time data updates.
4. Critically evaluate and provide constructive feedback on visualizations, considering clarity, accessibility, interactivity, and appropriateness of design choices using peer review and established frameworks.
5. Explain major concepts and techniques from the field of information visualization research in order to identify areas of contribution and apply appropriate evaluation methods (e.g., heuristic evaluation, user studies, insight-based metrics).
6. Plan and implement a visualization project, including defining a guiding question, modeling the data, iterating, conducting user evaluations, and presenting insights in a final interactive prototype.

What to expect? Course Format

This class is very hands-on heavy and participants are required to bring their laptops to class fully charged. In this class we’ll focus on skills that require practice, iterations, and feedback, aspects that are harder to get in books and online tutorials. With the stepping stones from this class you will be fully equipped to learn on your own time about the technicalities of more complex interactive visualizations, which once sketched, designed, prototyped becomes an iterative designing, implementing, and evaluating process.

For each assignment there isn’t a single correct answer, your earlier sketches and iterations will be as important for your learning process as the final deliverable. In this class you’ll learn an approach and skills for thinking about communicating data visually. There are important objective frameworks and concepts to learn, practice, and apply in your work.

This course will aim at the ILOs through the following structure.

• Weeks 1–2: Learn to see and communicate (why to visualize, how perception works).
• Weeks 3–4: Learn to design, iterate, and build (principles, tools, animation).
• Weeks 5–6: Learn to narrate (narrative, ethics).
• Weeks 7–10: Learn to contribute and evaluate (advanced applications, projects).

Each week includes a combination of lectures, labs, readings, homework/project and corresponding critiques.

Students can use the following agenda to organize their homework time.

• Deliverables due on Mondays are due at midnight.
• Deliverables due on Wednesdays are due at 2:30 pm (1h after the lab ends).

Student and Instructor Expectations

We are co-creating this quarter-long learning experience. Making sure the students hit the Intended Learning Outcomes is my priority. Should you have ideas on how to enhance the learning experience for the class, I welcome a conversation and feedback. For class content, whenever you have any questions or anything you'd like to discuss, please come to our office hours.

Communication. If you need to contact the instructor or reader/grader beyond office hours, you can email them directly. We will respond within 48 hours (excluding the weekends). We will notify you in advance if we are traveling for research, and this may cause a delay in our response to Canvas messages or emails. Requirement: Start the subject line of your messages with "INFO 474" so that we do not miss them.

We expect all instructors and students to be respectful in all communication and we’ll hold each other accountable to the iSchool IDEAS values.

Lecture slides. Slides from the lecture will be posted after class. This is an adaptive class so that the content may change throughout the quarter.

Assignments

Individual Homeworks: For individual assignments, students are encouraged to ask questions of the instructor or TAs during lab sessions and office hours. Collaborative discussion of course concepts with peers is allowed and even encouraged; however, each student must produce a submission that is uniquely their own, demonstrating an individual approach to applying the concepts.

Homework 4 and 5: Unlike the earlier assignments, which are submitted once, Homeworks 4 and 5 include a mid-assignment submission focused on sketching and design. The final submission will then be evaluated based on both the initial designs and the subsequent progress made. Grading will emphasize three dimensions: completeness, quality of code, and the depth of iteration in design. Each assignment will include its own Gen AI policy.

Final Project: The final project will be completed in teams of 3 students. Visualizations must be implemented in P5.js, while exploratory data analysis and preprocessing may be conducted in any other language (e.g., Python). All team members will receive the same grade; however, each student must document their individual contributions to ensure an equitable division of labor. Each team is also required to gather feedback on their visualization from external reviewers equal to twice the number of team members (these reviewers may not be classmates in this course).

Project evaluation will place emphasis on sketches and iterative design, which will be tracked through GitHub commits.

Grading

This class values completion, design, iteration, implementation, participation, and core concepts studied in class. Assignments will be evaluated in those dimensions. Students are encouraged to remain curious, ask questions, and engage actively with both the material and their peers.

Grading Breakdown

• Participation: 20%
• 12%: Completion of Lab check-offs (4 labs, 3% each).
•   8%: In-class presentations and peer-feedback (4 sessions, 2% each).
• Homeworks: 50%
• Final Project: 30%

Grading Conversion

This course follows the  iSchool Grading Scales, which converts percentage grades to a 4.0 scale.

Extra Credit

Extra credit opportunities (up to 3%) may be offered during the term and will be announced when available.

Late Work

• Students have two free late days to use across any of the five homeworks.
• Beyond that, 10% per day will be deducted from the assignment grade.
• If unexpected circumstances arise, students must request an extension at least one week in advance to opt for full credit; otherwise these will not be accepted.

Class Portfolio

 Starting with Homework 4, all homeworks must be published in a Github portfolio (public page) dedicated for this class. You will be asked to share the last submission commit on the live page. Please use branches (main for the most updated homework, dev for the current work in progress).

In Lab 1: Github & Class Portfolio you’ll get a walk through of how to set this up.

Course Material

Students will find recommended course material in the specific slide deck for each lecture. In addition to those, core resources are:

• Munzner, T. (2016). Visualization analysis and design (with illustrations by E. Maguire). CRC Press. Link.
• Tufte, E. R. (1983). The visual display of quantitative information. Graphics Press. Link.
• Cairo, A. (2013). The functional art: An introduction to information graphics and visualization. New Riders. Link.
• Ware, C. (2021). Information visualization: Perception for design. Morgan Kaufmann. Link.
• Afonso, A. I. (Ed.). (2024). Diagrams of power: Visualizing, mapping, and performing resistance. Set Margins’. Link

Collaborating with Generative AI

In situations where an assignment doesn’t include its own Gen AI policy, the default policy is:

1. do not use Generative AI to do your work for you. Using generative AI very carefully might be acceptable after you try your best to think critically, study, write, solve problems, code, answer questions, and debug.
2. report in your work any significant use of sophisticated tools, such as instruments and software; we now include in particular text-to-text or multimodal generative AI among those that should be reported consistent with subject standards for methodology.
3. remind you that each of you individually take full responsibility for all the submission contents, irrespective of how the contents were generated. If generative AI language tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in your work, it is your responsibility.
4. generative AI language tools should not be listed as an author; instead authors should refer to (2).

Academic Conduct

Please review the iSchool Academic Policies.

The University takes academic integrity very seriously. Behaving with integrity is part of our responsibility to our shared learning community. If you’re uncertain about if something is academic misconduct, ask me. I am willing to discuss questions you might have.

Acts of academic misconduct may include but are not limited to:

• Cheating (working collaboratively on homeworks/midterm and discussion submissions when collaboration is not allowed, and previewing quizzes/exams)
• Plagiarism (representing the work of others–human or AI–as your own without giving appropriate credit to the original author(s))
• Any Unauthorized collaboration

Concerns about these or other behaviors prohibited by the Student Conduct Code will be referred for investigation and adjudication by (include information for specific campus offices). Students found to have engaged in academic misconduct may receive a zero on the assignment (or other possible outcome).

IDEAS

From the IDEAS section of the iSchool website: We create an environment that fosters appreciation, mutual respect, and engagement among and between members of the iSchool, UW community, and beyond, with special attention to the needs of people from historically marginalized communities. We envision a university in which all students, faculty and staff participate fully and meaningfully in campus life without being subjected to discrimination, bias or microaggressions. We condemn any expressions of racism, sexism, homophobia, transphobia, ableism, or any other instances of bias and discrimination against marginalized individuals or groups.

Wellbeing and Support Resources

Your wellbeing is important. There may be times when stress, life challenges, or difficult emotions affect your academic experience and daily life. If this happens, please know you are not alone, and support is available.​

The University of Washington Counseling Center (mentalhealth.uw.edu | 206-543-1240) offers free and confidential services during business hours to support you with stress, adjustment, or mental health concerns. For 24/7 support, you can call the Husky Helpline at 206-616-7777 or the National Suicide & Crisis Lifeline by dialing 9-8-8.​ For students enrolled in an Information School program, you may reach out to book a confidential appointment with the Mental Health Counselor, Leigh Eisele, at leisele1@uw.edu.

Reaching out for support is a sign of strength. Whether you're seeking tools to manage stress, someone to talk with, or resources for a friend, help is here.

Excused Absence from Class

Students are expected to attend class and to participate in all graded activities, including midterms and final examinations. A student who is anticipating being absent from class due to a Religious Accommodation activity needs to complete the Religious Accommodations request process by the second Friday of the quarter. Students who anticipate missing class due to attendance at academic conferences or field trips, or participation in university-sponsored activities should provide a written notice to the instructor with two weeks of notice ahead of the absence. The instructor will determine if the graded activity or exam can be rescheduled or if there is equivalent work that can be done as an equivalent, as determined by the instructor.

Religious Accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Acknowledgments

The current version of this course builds on information visualization practices and theory developed by world-leading artists, practitioners, and educators. In particular, built from the following previous classes:

• UW CSE 412: Data Visualizations, Tal Wolman.
• MIT 4.032 / 4.033: Information Design & Visualization, Ben Fry.
• MIT 6.894: Interactive Data Visualization Arvind Satyanarayan.
• CMU 60212: Interactivity & Computation, Golan Levin.
• UW CSE 412: Intro to Data Visualization, Jane Hoffswell.
• UW INFO 474: Interactive Information Visualization, Jasper Maynard-Zhang.
• Thanks also to computational artist Char Stiles, visualizer engineers Denis Jen @ Pattern Institute, and Ali Klemencic @ MIT CCC, for workshopping resources and the current syllabus.