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 and algorithms for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. Students will learn how to design and build interactive visualizations for the web, using the Vega-Lite and D3.js (Data-Driven Documents) frameworks.
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.
- Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press.
- Vega-Lite Visualization Notebook Curriculum. Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craft.
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 applications.
Schedule & Readings
- REQUIRED Notebook: Introduction to Vega-Lite.
- REQUIRED Chapter 1: Information Visualization, in Readings in Information Visualization. Stuart Card, Jock Mackinlay, and Ben Shneiderman. 1999.
- Optional Decision to Launch the Challenger, in Visual Explanations. Edward Tufte. (Importantly, see also a critique of Tufte's argument.)
- REQUIRED Notebook: Data Types, Graphical Marks, and Visual Encoding Channels.
- Optional The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Ben Shneiderman. Proc. IEEE Conference on Visual Languages, 1996.
- REQUIRED Pre-recorded Lecture Videos (Parts 1-3, small multiples in Part 4)
- REQUIRED Notebook: Data Transformation.
- REQUIRED Chapter 3: The Power of Representation, in Things That Make Us Smart. Don Norman.
- REQUIRED Chapters 2, 4 in Interactive Data Visualization for the Web, 2nd Edition. Scott Murray.
- REQUIRED Notebook: Data Transformation.
- REQUIRED Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Chris Stolte, Diane Tang, and Pat Hanrahan. IEEE Transactions on Visualization and Computer Graphics, 2002.
- Optional Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations. Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe & Jeffrey Heer. IEEE Transactions on Visualization and Computer Graphics, 22(1), 649-658, 2016.
- Optional Exploratory Data Analysis, NIST Engineering Statistics Handbook.
- REQUIRED Notebook: Cartographic Visualization.
- REQUIRED Chapter 11: The Cartogram: Value-by-Area Mapping, in Cartography: Thematic Map Design. Dent.
- REQUIRED Chapters 5 in Interactive Data Visualization for the Web, 2nd Edition. Scott Murray.
- REQUIRED Notebook: Introduction to D3, Part 1.
- REQUIRED Chapters 6, 7, 8 in Interactive Data Visualization for the Web, 2nd Edition. Scott Murray.
- Optional D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky & Jeffrey Heer. InfoVis 2011.
- Optional Vega-Lite: A Grammar of Interactive Graphics. K. Wongsuphasawat, D. Moritz, A. Satyanarayan & J. Heer. OpenVis Conf 2017.
- 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!)
- REQUIRED Chapters 9, 10 in Interactive Data Visualization for the Web, 2nd Edition. Scott Murray.
- REQUIRED Easing Functions Cheat Sheet.
- REQUIRED Animated Transitions in Statistical Data Graphics. Jeffrey Heer & George Robertson. IEEE InfoVis 2007.
- REQUIRED Chapters 11, 12 in Interactive Data Visualization for the Web, 2nd Edition. Scott Murray.
- Optional Effectiveness of Animation in Trend Visualization. George Robertson, Roland Fernandez, Danyel Fisher, Bongshin Lee, & John Stasko. InfoVis 2008.
- REQUIRED Color Use Guidelines for Data Representation. Cynthia Brewer. Proc. Section on Statistical Graphics, American Statistical Association, pp. 55-60, 1999. Color Scheme Explorer.
- REQUIRED How to pick more beautiful colors for your data visualizations. Lisa Charlotte Rost.
- Optional Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. Yang Liu, Jeffrey Heer. ACM CHI 2018.
- Optional D3 color scales: d3-scale-chromatic
- 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.
- REQUIRED Perception in Visualization. Christopher Healey.
- REQUIRED 39 Studies About Human Perception in 30 Minutes. Kennedy Elliott.
- 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.
- REQUIRED Squarified Treemaps. Mark Bruls, Kees Huizing & Jarke van Wijk. Eurographics Data Visualization 2000.
- REQUIRED The Barnes-Hut Approximation: Efficient computation of N-body forces. Jeffrey Heer. 2017.
- Optional Scalable, Versatile and Simple Constrained Graph Layout. Tim Dwyer. EuroVis 2009.
- Optional Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006.
- Optional Use the Force! Michael Bostock. 2011. (Video)
- Optional d3-hierarchy. Michael Bostock.
- Optional d3-force. Michael Bostock.
- 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.
- REQUIRED Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations. Dominik Moritz, Bill Howe, Jeffrey Heer. ACM CHI 2019.
- Optional imMens: Real-time Visual Querying of Big Data. Zhicheng Liu, Biye Jiang, Jeffrey Heer. EuroVis 2013.
- 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.
- Class Participation 10%
- Assignment 1: Expository Visualization 10%
- Assignment 2: Deceptive Visualization 15%
- Assignment 2: Peer Review 5%
- Assignment 3: Interactive Visualization 20%
- Assignment 3: Peer Review 5%
- Final Project 35%
Late Policy: You have two (2) late days that you can apply as needed to turn in an individual assignment (A1, A2, Peer Reviews) after the due date without penalty. Each project team also has an additional two (2) late days for group assignments (A3, FP). Beyond that, we will deduct 10% for each day an assignment is late. Please contact the instructors prior to a deadline if you intend to apply your late days or if you would like to request additional accommodations.
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.
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.
It is important to attend the lectures (or if you are unable, to watch the recordings) and read the readings. Each lecture will assume that you have read and are ready to discuss the day's readings.
Class participation includes both in-lecture activities (as is feasible) and engagement on the course discussion site (Ed). Up through week 8, 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 1 pass 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
In addition, we will post short quizzes to reinforce important concepts. The quizzes are not graded – your score on a quiz will not affect your course grade – but you are required to complete the quiz as part of your course participation.
See the resources page for visualization tools, related web sites, and software development tips.
Questions should be posted on the course discussion site (Ed). If you have a private question, please email the instructors at cse442@cs or discuss it at office hours.