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Instructor: Tamara Munzner
First Class: Thu Sep 4 2025
Time: Thu 2-5pm
Location: SCRF 1021
Office Hours: Thu 5pm in classroom, until all questions are handled. Or zoom by appointment, check my calendar and request a spot by email.

Canvas: 547-25 Canvas
Piazza: 547-25 Piazza (signup link) Please join Piazza, it's a main avenue of communication for this course!

Visualization Analysis and Design, Tamara Munzner (A K Peters Visualization Series, CRC Press, 2014) is the course textbook.

The UBC library has multiple ebook licenses so you can read it for free: library catalog page, direct link (must be logged in).

To buy your own copy online, it's currently around US$68 new (with shipping) from amazon.com, on sale for US$63 (free shipping) directly from CRC Press, and CD$124 from amazon.ca. If you buy the hardcover book, you'll get the ebook version for free; you can also buy an ebook-only for less, or rent the ebook for even less.

All additional readings are research papers available online for free, links posted below. For digital library access from off-campus, use OpenAthens with your CWL login through the UBC library.
OpenAthens direct links: IEEE DL, Springer DL, ACM DL, JSTOR

• VAD Chapter 1. What's Vis, and Why Do It?
• Course Logistics
  • main/syllabus (this page!)
  • course structure
  • project structure

Online Discussion: see Piazza posts @8 self-intros, @10 reading list, @11 Ch 1 comments

In Class Exercise: Time Series
Signup Sheet for in-person attendance

Further Reading

• Ch 1 (Why Do It?) Further Reading
• In-Class Exercise Followup Videos & Papers
  • Cluster-Calendar paper
  • Ocupado video, paper
  • ChronoLenses video, paper
  • Stack Zooming video, paper
  • LiveRAC video, paper

• VAD Chapter 2. What: Data Abstraction
• VAD Chapter 3. Why: Task Abstraction
• paper A Nested Model for Visualization Design and Validation. Tamara Munzner. IEEE TVCG (Proc. InfoVis 2009) 15(6):921-928, 2009.
[type: model]

Online Discussion: see Piazza posts @12 reading list, @13 Ch 2 comments @14 Ch 3 comments @15 Nested Model comments

Slides: pdf, pdf 16up, keynote

In Class Exercise: Abstraction
Signup Sheet for in-person attendance

Further Reading

• Ch 2 (Data Abstraction) Further Reading
• Ch 3 (Task Abstraction) Further Reading

• VAD Chapter 5. Marks and Channels
• VAD Chapter 6. Rules of Thumb
• paper Design Study Methodology: Reflections from the Trenches and the Stacks. Michael Sedlmair, Miriah Meyer, and Tamara Munzner. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2012), 18(12):2431-2440, 2012. [type: model]

Online Discussion: see Piazza posts @16 reading list, @17 Ch 5 comments @18 Ch 6 comments @19 Design Study Methodology comments

Slides: pdf, pdf 16up, keynote

Further Reading

• Ch 5 (Marks & Channels) Further Reading
• Ch 6 (Rules of Thumb) Further Reading
• pointers to many design studies

• VAD Chapter 7. Arrange Tables
• VAD Chapter 11. Manipulate View
• TACO: Visualizing Changes in Tables Over Time. Christina Niederer, Holger Stitz, Reem Hourieh, Florian Grassinger, Wolfgang Aigner, Marc Streit. IEEE TVCG (InfoVis '17), 24(1): 677-686, doi:10.1109/TVCG.2017.2745298, 2018. [type: technique]

Online Discussion: see Piazza posts @22 reading list, @23 Ch 7 comments @24 Ch 11 comments @25 Taco comments

Slides: pdf, pdf 16up, keynote

In Class: Project Pitches

Further Reading

• Ch 7 (Table Data) Further Reading
• Ch 11 (Manipulate) Further Reading

• Tools guidance
• VAD Chapter 12. Facet into Multiple Views
• VAD Chapter 13. Reduce Items and Attributes
• paper Pattern-Driven Navigation in 2D Multiscale Visualizations with Scalable Insets. Fritz Lekschas, Michael Behrisch, Benjamin Bach, Peter Kerpedjiev, Nils Gehlenborg, and Hanspeter Pfister. IEEE TVCG 26(1):611-621, 2020 (Proc InfoVis 2019). [type: technique]
  • Videos

Online Discussion: see Piazza posts @27 reading list, @28 Ch 12 comments @29 Ch 13 comments @30 Scalable Insets comments

Slides: pdf, pdf 16up, keynote

In Class Exercise: Complexity Families
Followup reading:
Animation vs traces vs small multiples: Effectiveness of Animation in Trend Visualization. Robertson, Fernandez, Fisher, Lee, and Stasko. TVCG/InfoVis 2008.
EdgeMaps: Visualizing Explicit and Implicit Relations of Complex Information Spaces. Dörk, Carpendale, Williamson. Information Visualization 2012.

Further Readings

• Ch 12 (Multiple Views) Further Reading
• Ch 13 (Reduce) Further Reading

• VAD Chapter 14. Embed: Focus+Context
• paper Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, and Martin Wattenberg. IEEE TVCG (Proc. VAST 2017) 24(1):1-12, 2018. [type: design study]
• paper Dashboard Design Patterns. Benjamin Bach, Euan Freeman, Alfie Abdul-Rahman, Cagatay Turkay, Saiful Khan, Yulei Fan, and Min Chen. IEEE TVCG (Proc. VIS 2022) 29(1):342-352, 2023. [type: model]
  • Supplemental materials

• Ch 14 (Embed) Further Reading

• VAD Chapter 10. Map Color and Other Channels
• paper Evaluation of Artery Visualizations for Heart Disease Diagnosis. Michelle A. Borkin, Krzysztof Z. Gajos, Amanda Peters, Dimitrios Mitsouras, Simone Melchionna, Frank J. Rybicki, Charles L. Feldman, and Hanspeter Pfister. IEEE TVCG (Proc. InfoVis 2011), 17(12):2479-2488, 2011. [type: empirical / design study]
• paper Modeling color difference for visualization design. Danielle Albers Szafir IEEE TVCG (Proc. InfoVis 2017) 24(1):392-401, 2018. DOI:10.1109/TVCG.2017.2744359 [type: empirical]

• Ch 10 (Color) Further Reading
• Santiago Ortiz, 45 Ways to Communicate Two Quantities
• Nadieh Bremer, A sky map of the Hubble Space Telescope's observations (detailed design walkthrough, includes substantial discussion of color palettes)

• VAD Chapter 8. Arrange Spatial Data
• paper Necklace Maps. Bettina Speckmann and Kevin Verbeek. IEEE TVCG (Proc. InfoVis 2010) 16(6):881-889. [type: algorithm]
• paper Unfolding the Earth: Myriahedral Projections. Jarke J. van Wijk. The Cartographic Journal, 45(1):32-42, 2008. [type: algorithm]
  • Video
  • Demo

Further Readings

• Ch 8 (Spatial Data) Further Reading

• VAD Chapter 9. Arrange Networks
• paper ABySS-Explorer: visualizing genome sequence assemblies.. Cydney B. Nielsen, Shaun D. Jackman, Inanc Birol, Steven J.M. Jones. TVCG 15(6):881-8, 2009 (Proc. InfoVis 2009). [type: design study]
• paper Interactive Visualization of Genealogical Graphs. Michael J. McGuffin, Ravin Balakrishnan. Proc. InfoVis 2005, pp 17-24. [type: technique]
  • video

Further Reading

• Ch 9 (Network Data) Further Reading

• paper Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Chris Stolte and Pat Hanrahan. Proc. InfoVis 2000. [type: system]
• paper D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky, Jeffrey Heer. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2011. [type: system]
• paper Vega-Lite: A Grammar of Interactive Graphics. Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2016. [type: system]

In Class: NO CLASS (IEEE VIS Conference week)

Further Reading

• paper A layered grammar of graphics. Hadley Wickham. Journal of Computational and Graphical Statistics 19(1):3-28, 2010. [type: system/model] (R/tidyverse)
• D3
  • D3.js A powerful JavaScript library for manipulating documents based on data
  • Interactive Data Visualization for the Web - An Introduction to Designing with D3 by Scott Murray (2nd edition, D3 v4)
  • Technology fundamentals for D3.js (Slides by K. Wongsuphasawat, J. Hoffswell, D. Moritz)
  • Intro to D3
  • See the resource page for a longer discussion of readings for D3 (and a few other tools as well).

In Class: Project Peer Reviews

In Class: Lecture, Evals

Audience

• There are no enforced prerequisites. Grad students from other departments are very welcome. It is possible to for students in other disciplines to take this course without a programming background, by picking an analysis or survey project.
• My past experience in teaching this course shows that students need strength in at least 1 and preferably 2 of these 3 skills to succeed: programming, English, HCI.
• Strong English communication skills are important for success in this course: there are substantial reading, writing, discussion, and presentation components in the required work.
• Many of the project types require programming, so that skill will give you the broadest possible set of options. However, there are no enforced prerequisites, and grad students from other departments are very welcome. It's possible to for students in disciplines outside of CS to take this course without a programming background, by picking an analysis or survey project.
• HCI (human-computer interaction) is helpful but not required; it's useful to take CPSC 544 (fine to do so simultaneously this term), or have taken an equivalent methods course elsewhere.
• Many other skills are useful, including computer graphics, cognitive psychology, machine learning, statistics, or algorithms. Also useful is expertise in a particular application domain if you intend to do your project in it (bioinformatics, computer systems, etc).
• I have found that students with weak backgrounds in all three of these areas have often not succeeded in previous years: weak programming skills, weak English (for example, if English is your second language and you struggle with fluency), and no HCI background is not a recipe for success.
• This graduate course does not teach visualization libraries: most students will pick up D3 (Javascript), ggplot (R), or python-based visualization tooling on their own.
• For most undergrads, the 4th year undergrad visualization course, next offered in January 2026, is probably a better match; D3 will be explicitly taught and a programming background is required. If you nevertheless feel strongly that this course is the right one for you, email me and we'll discuss whether your background is a good fit.
• Although the class is typically officially full at the start of term and often there's a waitlist, don't worry if you can't register: do show up anyway. In grad CS classes there is typically substantial churn in the first few weeks as people shop around, so it's extremely likely that you'll be able to get in. The waitlist has always been cleared in the past. I will give priority to people who have been attending all along and are caught up.
• I'm open to unofficial auditors, there are usually a few each year. You'll get out what you put in: please do the reading for that session, so that you can participate in the in-class exercises and discussions. It's OK to come for the first several weeks and then stop if you get too busy with other things, but tell me that you're dropping it so that there aren't imbalances in the small-group breakouts that I may assign before class.

Previous Versions

• Information Visualization, Fall 2022
• Information Visualization, Fall 2021
• Information Visualization, Fall 2020
• Information Visualization, Fall 2019
• Information Visualization, Fall 2017
• Information Visualization, Spring 2017
• Information Visualization, Fall 2015
• Information Visualization, Fall 2014
• Information Visualization, Fall 2011
• Information Visualization, Fall 2009
• Information Visualization, Fall 2007
• Information Visualization, Fall 2006
• Information Visualization, Fall 2005
• Information Visualization, Fall 2004
• Information Visualization, Spring 2004
• Information Visualization, Spring 2003