The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Furthermore, visual representations may help engage more diverse audiences in the process of analytic thinking.
In this course we will study techniques for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.
In addition to class discussions, students will complete visualization design and data analysis assignments, as well as a final project. Students will share the results of their final project as both an interactive website and a video presentation.
Learning Goals & ObjectivesThis course is designed to provide students with the foundations necessary for understanding and extending the current state of the art in data visualization. By the end of the course, students will have gained:
- An understanding of key visualization techniques and theory, including data models, graphical perception and methods for visual encoding and interaction.
- Exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, geo-spatial data, and networks.
- Practical experience building and evaluating visualization systems.
- The ability to read and discuss research papers from the visualization literature.
- The Visual Display of Quantitative Information, E. Tufte. Graphics Press, 2001.
- Envisioning Information, E. Tufte. Graphics Press, 1990.
- Vega-Lite Visualization Notebook Curriculum. Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craft.
- Optional: Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press.
See the resources page for visualization tools, related web sites, and software development tips.
Assignments & Grades
- Class Participation (in-lecture activities, discussion posts, quizzes) 10%
- Assignment 1: Visualization Design 10%
- Assignment 2: Deceptive Visualization 15%
- Assignment 3: Interactive Visualization 25%
- Final Project 40%
Schedule & Readings
- REQUIRED Chapter 1: Information Visualization, in Readings in Information Visualization. Stuart Card, Jock Mackinlay, and Ben Shneiderman. 1999.
- Optional The Value of Information Visualization. Jean-Daniel Fekete, Jarke J. van Wijk, John T. Stasko, and Chris North. Information visualization, pp. 1-18. Springer, Berlin, Heidelberg, 2008.
- Optional Decision to Launch the Challenger, in Visual Explanations. Edward Tufte. (See also a critique of Tufte's argument.)
- REQUIRED Chapter 1: Graphical Excellence, In The Visual Display of Quantitative Information. Tufte.
- REQUIRED Chapter 2: Graphical Integrity, In The Visual Display of Quantitative Information. Tufte.
- REQUIRED Chapter 3: Sources of Graphical Integrity, In The Visual Display of Quantitative Information. Tufte.
- Optional The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations, Shneiderman, Proc. IEEE Conference on Visual Languages, Boulder 1996.
- REQUIRED Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Stolte, Tang, and Hanrahan. IEEE TVCG 2002.
- Encouraged Chapter 4: Data-Ink and Graphical Redesign, In The Visual Display of Quantitative Information. Tufte.
- Encouraged Chapter 5: Chartjunk, In The Visual Display of Quantitative Information. Tufte.
- Encouraged Chapter 6: Data-Ink Maximization and Graphical Design, In The Visual Display of Quantitative Information.
- Optional Battle, L. and Heer, J., 2019, June. Characterizing exploratory visual analysis: A literature review and evaluation of analytic provenance in tableau. In Computer graphics forum (Vol. 38, No. 3, pp. 145-159).
- REQUIRED Interactive Dynamics for Visual Analysis. Jeffrey Heer & Ben Shneiderman. 2012.
- Optional Dimara, E. and Perin, C., 2019. What is interaction for data visualization?. IEEE transactions on visualization and computer graphics, 26(1), pp.119-129.
- Optional The Death of Interactive Infographics? Dominikus Baur. 2017.
- Optional In Defense of Interactive Graphics. Gregor Aisch. 2017.
- Video Classic systems on stat-graphics.org
- REQUIRED Design and Redesign in Data Visualization. Martin Wattenberg and Fernanda Viégas. 2015.
- Encouraged Chapter 8: Data Density and Small Multiples, In The Visual Display of Quantitative Information. Tufte.
- Encouraged Chapter 2: Macro/Micro Readings, In Envisioning Information. Tufte.
- Encouraged Chapter 4: Small Multiples, In Envisioning Information. Tufte.
- REQUIRED Battle, L. and Scheidegger, C., 2020. A Structured Review of Data Management Technology for Interactive Visualization and Analysis. IEEE Transactions on Visualization and Computer Graphics.
- REQUIRED imMens: Real-time Visual Querying of Big Data. Zhicheng Liu, Biye Jiang, Jeffrey Heer. EuroVis 2013.
- Optional Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations. Dominik Moritz, Bill Howe, Jeffrey Heer. ACM CHI 2019.
- Optional Dynamic Prefetching of Data Tiles for Interactive Visualization. Leilani Battle, Remco Chang, Michael Stonebraker. SIGMOD 2016.
- Optional Trust, but Verify: Optimistic Visualizations of Approximate Queries for Exploring Big Data. Dominik Moritz, Danyel Fisher, Bolin Ding, Chi Wang. ACM CHI 2017.
- REQUIRED Notebook: Introduction to D3, Part 1.
- REQUIRED D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky & Jeffrey Heer. InfoVis 2011.
- Optional Mannino, M. and Abouzied, A., 2018, April. Expressive time series querying with hand-drawn scale-free sketches. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
- Optional Liu, Z., Thompson, J., Wilson, A., Dontcheva, M., Delorey, J., Grigg, S., Kerr, B. and Stasko, J., 2018, April. Data Illustrator: Augmenting vector design tools with lazy data binding for expressive visualization authoring. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
- Optional Vega-Lite: A Grammar of Interactive Graphics. K. Wongsuphasawat, D. Moritz, A. Satyanarayan & J. Heer. OpenVis Conf 2017 Presentation.
- Optional Vega-Lite: A Grammar of Interactive Graphics. Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat & Jeffrey Heer. IEEE InfoVis 2016.
- REQUIRED Notebook: Introduction to D3, Part 2. (Note: we will work through this in class, but we encourage you to skim it ahead of time!)
- Optional Chapters 9, 10 in Interactive Data Visualization for the Web, 2nd Edition. Scott Murray.
- REQUIRED Effectiveness of Animation in Trend Visualization. George Robertson, Roland Fernandez, Danyel Fisher, Bongshin Lee, & John Stasko. InfoVis 2008.
- REQUIRED Easing Functions Cheat Sheet.
- Optional Animated Transitions in Statistical Data Graphics. Jeffrey Heer & George Robertson. IEEE InfoVis 2007.
- Optional Animation: Can It Facilitate? Barbara Tversky, Julie Morrison, Mireille Betrancourt, International Journal of Human Computer Studies, v57, p247-262. 2002.
- Optional Smooth and Efficient Zooming and Panning. Jack J. van Wijk and Wim A.A. Nuij. IEEE InfoVis 2003.
- REQUIRED Which color scale to use when visualizing data. Lisa Charlotte Rost.
- REQUIRED Szafir, D.A., 2017. Modeling color difference for visualization design. IEEE transactions on visualization and computer graphics, 24(1), pp.392-401.
- Optional Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. Yang Liu, Jeffrey Heer. ACM CHI 2018.
- Optional Color Use Guidelines for Data Representation. Cynthia Brewer. Proc. Section on Statistical Graphics, American Statistical Association, pp. 55-60, 1999. Color Scheme Explorer.
- Optional Colorgorical: Creating Discriminable and Preferable Color Palettes for Information Visualization. Connor Gramazio, David Laidlaw & Karen Schloss. IEEE Transactions on Visualization and Computer Graphics. 2017.
- Optional D3 color scales: d3-scale-chromatic
- REQUIRED Perception in Visualization. Christopher Healey.
- REQUIRED 39 Studies About Human Perception in 30 Minutes. Kennedy Elliott.
- REQUIRED Chapter 3: Layering and Separation, In Envisioning Information. Tufte.
- Optional Graphical Perception: Theory, Experimentation and the Application to the Development of Graphical Models. William S. Cleveland, Robert McGill. J. Am. Stat. Assoc. 79(387):531-554, 1984.
- Optional Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. Jeffrey Heer, Michael Bostock. ACM CHI 2010.
- REQUIRED The Visual Uncertainty Experience. Jessica Hullman. OpenVis Conf 2016.
- REQUIRED Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. Michael Correll, Michael Gleicher. IEEE InfoVis 2014.
- Optional Visual Semiotics & Uncertainty Visualization: An Empirical Study. Alan MacEachren, Robert Roth, James O'Brien, Bonan Li, Derek Swingley, Mark Gahegan. IEEE InfoVis 2012.
- Optional When(ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. Matthew Kay, Tara Kola, Jessica Hullman, Sean Munson. ACM CHI 2016.
- Optional Jena, A., Engelke, U., Dwyer, T., Raiamanickam, V. and Paris, C., 2020, June. Uncertainty visualisation: An interactive visual survey. In 2020 IEEE Pacific Visualization Symposium (PacificVis) (pp. 201-205). IEEE.
- REQUIRED Herman, I., Melançon, G. and Marshall, M.S., 2000. Graph visualization and navigation in information visualization: A survey. IEEE Transactions on visualization and computer graphics, 6(1), pp.24-43.
- Optional Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006.
- Optional Squarified Treemaps. Mark Bruls, Kees Huizing & Jarke van Wijk. Eurographics Data Visualization 2000.
- Optional Scalable, Versatile and Simple Constrained Graph Layout. Tim Dwyer. EuroVis 2009.
- Optional ManyNets: An Interface for Multiple Network Analysis and Visualization. Freire et al. ACM CHI 2010.
- Optional Use the Force! Michael Bostock. 2011. (Video)
- Optional Beck, F., Burch, M., Diehl, S. and Weiskopf, D., 2017, January. A taxonomy and survey of dynamic graph visualization. In Computer Graphics Forum (Vol. 36, No. 1, pp. 133-159).
- REQUIRED Narrative Visualization: Telling Stories with Data. Edward Segel & Jeffrey Heer. InfoVis 2010.
- REQUIRED Reinventing Explanation. Michael Nielsen, 2014.
- Optional GraphScape: A Model for Automated Reasoning about Visualization Similarity and Sequencing. Younghoon Kim, Kanit Wongsuphasawat, Jessica Hullman, Jeffrey Heer. ACM CHI 2017.
- REQUIRED Lam, H., Bertini, E., Isenberg, P., Plaisant, C. and Carpendale, S., 2011. Empirical studies in information visualization: Seven scenarios. IEEE transactions on visualization and computer graphics, 18(9), pp.1520-1536.
- REQUIRED A Nested Model for Visualization Design and Validation. Tamara Munzner. IEEE InfoVis 2009.
- Optional Design Study Methodology: Reflections from the Trenches and the Stacks. Michael Sedlmair, Miriah Meyer & Tamara Munzner. IEEE InfoVis 2012.
- Optional Zeng, Z., Moh, P., Du, F., Hoffswell, J., Lee, T.Y., Malik, S., Koh, E. and Battle, L., 2021. An Evaluation-Focused Framework for Visualization Recommendation Algorithms. IEEE Transactions on Visualization and Computer Graphics, 28(1), pp.346-356.
- Optional Xu, K., Ottley, A., Walchshofer, C., Streit, M., Chang, R. and Wenskovitch, J., 2020, June. Survey on the analysis of user interactions and visualization provenance. In Computer Graphics Forum (Vol. 39, No. 3, pp. 757-783).
Activities, Learning Assessments & Expectations for Students
Readings: To contribute to lively and insightful discussions, we each need to do our part to be prepared. Students are expected to complete assigned readings prior to lecture. All readings are listed in the course schedule.
Lectures: It is important to attend the lectures and read the readings. Each lecture will assume that you have read and are ready to discuss the day's readings. This quarter, all lectures will be in person and recorded. If you speak during a lecture, you will be included in the video. Keep this in mind as you attend the lectures!
Discussion Posts: Class participation includes both in-lecture activities (as is feasible) and engagement on the course discussion site (Ed). All enrolled students are required to submit at least 1 substantive discussion post per week related to the course readings or lecture material. Each student also has 2 passes for skipping comments.
Good comments typically exhibit one or more of the following:
- Critiques of arguments made in the papers
- Analysis of implications or future directions for work discussed in lecture or readings
- Clarification of some point or detail presented in the class
- Insightful questions about the readings or answers to other people's questions
- Links to web resources or examples that pertain to a lecture or reading
Quizzes: We will post short quizzes to reinforce important concepts. Quizzes are considered a part of course participation and are not graded (your score on a quiz will not affect your course grade).
Assignments: There will be three assignments to help prepare you for the final project. These assignments are designed to give students experience in creating and evaluating visualization tools. See the course calendar for assignment dates.
Final Project: A team-based final project is due at the end of the course, with periodic milestones throughout the quarter. The goal of the final project is to identify a new, interesting and challenging visualization problem, and to apply the techniques and skills learned in class to address this problem. Students are welcome to work on their own final project ideas for the course, but a list of potential final projects will also be shared through Canvas. Check the course calendar for final project milestone deadlines.
Commitment to Fostering an Inclusive Learning EnvironmentThis course welcomes all students of all backgrounds. The computer science and computer engineering industries have a significant lack of diversity. This is due to a lack of sufficient past efforts by the field toward even greater diversity, equity, and inclusion. The Allen School seeks to create a more diverse, inclusive, and equitable environment for our community and our field. You should expect and demand to be treated by your classmates and the course staff with respect. If any incident occurs that challenges this commitment to a supportive, diverse, inclusive, and equitable environment, please let the instructor know so the issue can be addressed.
Late Policy: We will deduct 10% for each day an assignment is late. Please contact the instructors well in advance to request extensions if needed.
Plagiarism Policy: Assignments should consist primarily of original work. Building off of others' work—including 3rd party libraries, public source code examples, and design ideas—is acceptable and in most cases encouraged. However, failure to cite such sources will result in score deductions proportional to the severity of the oversight.
Excused Absences: 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 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.
Medical Excuse Notes: Students are expected to attend class and to participate in all graded activities, including midterms and final examinations. To protect student privacy and the integrity of the academic experience, students will not be required to provide a medical excuse note to justify an absence from class due to illness. A student absent from any graded class activity or examination due to illness must request, in writing, to take a rescheduled examination or perform work judged by the instructor to be the equivalent. Students are responsible for taking any number of examinations for which they are scheduled on a given day and may not request an adjustment for this reason alone.
Religious Accommodation: 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 here: Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.
Course Management & Communication with Staff
Canvas & Ed Discussion: The class will be managed from Canvas. Please look there for updates and announcements throughout the quarter. If you need to reach out and communicate with course staff, please post on Ed Discussion, Canvas, or send an email to the course staff. You are likely to receive a faster response from us if you post questions, comments, and concerns directly through Ed Discussion. If you have a private question, please email the instructors at
Please feel free to reach out about personal, academic, and intellectual concerns/questions. However, please consult the syllabus or Canvas first for logistical questions, such as when an assignment is due or how much an assignment is worth in terms of grading.
Important Announcements: We will send all important course announcements through Canvas. You must make sure that your email and announcement notifications (including changes in assignments and/or due dates) are enabled in Canvas so you do not miss these messages. You are responsible for checking your email and Canvas inbox with regular frequency.
Questions should be posted on the course discussion site (Ed). If you have a private question, please email the instructors at cse512@cs or discuss it at office hours.
Additional Information & Resources
Disability Resources for Students: Your experience in this class is important to me. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law. If you have already established accommodations with Disability Resources for Students (DRS), please activate your accommodations via myDRS so we can discuss how they will be implemented in this course.
If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), contact DRS directly to set up an Access Plan. DRS facilitates the interactive process that establishes reasonable accommodations. Contact DRS at http://depts.washington.edu/uwdrs/.
Safe Campus: Call SafeCampus at 206-685-7233 anytime – no matter where you work or study – to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support, while discussing short- and long-term solutions and connecting you with additional resources when requested. More information can be found here: https://www.washington.edu/safecampus/.
Sex- and Gender-Based Violence and Harassment: University policy prohibits all forms of sexual harassment. If you feel you have been a victim of sexual harassment or if you feel you have been discriminated against, you may speak with your instructor, teaching assistant, the chair of the department, or you can file a complaint with the UW Ombudsman's Office for Sexual Harassment. Their office is located at 339 HUB, (206)543-6028. There is a second office, the University Complaint Investigation and Resolution Office, who also investigate complaints. The UCIRO is located at 22 Gerberding Hall.
Please see additional resources at http://www.washington.edu/about/ombudsman/role.html and http://f2.washington.edu/treasury/riskmgmt/UCIRO.