data visualization assignments

6.894 : Interactive Data Visualization

Starting Spring 2021, this class has been re-numbered as 6.859 .

Check out the Final Project Showcase »

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. Moreover, visual representations may help engage more diverse audiences in the process of analytic thinking.

By the end of this course, you should expect to be able to:

• Design, evaluate, and critique visualization designs.
• Wrangle and explore datasets through visualization using Trifacta Wrangler and Tableau .
• Understand visualization techniques and theory.
• Implement interactive data visualizations using Vega-Lite , and D3.js .
• Develop a substantial visualization project.

Schedule & Readings

Week [$index + 1] : [theme].

• Assigned Due [name]
• Required Optional . . . .

[semester] · [schedule] ([location])

Teaching Staff

[email protected]

A1: Visualization Design 5%

A2: Exploratory Data Analysis 10%

A3: White/Black Hat Visualization 15%

A4: Interactive Visualization 20%

Final Project 40%

Class Participation 5%

Readings/Exercises 5%

Individual assignments. The first three assignments are solo assignments, and should be completed without collaboration. You are encouraged to ask the instructor and/or TAs for advice during office hours, and to use Piazza to obtain answers to questions from other students.

Team projects. Team projects, of course, encourage collaboration. You are encouraged to work together on all parts of the project, and must ensure that every team member is involved in all aspects of the project (design, coding, and documentation). Although the team will receive a single grade, each team member will be asked to identify their own work product to ensure equitable divison of labor. Participation in team check-in meetings and project presentations will be evaluated on an individual basis.

Reuse of third-party material. Unless otherwise stated in an assignment, you are free to use any third-party code, whether as libraries or code fragments, and to adopt any idea you find online or in a book as long as it is publicly available and appropriately cited (see the section on code in the MIT Handbook on Academic Integrity for details).

Lateness. You have 4 slack days, which you can use as you wish for assignments 1–4. These days are to be used for minor illnesses, special occasions (such as religious holidays, interviews and sports meet events), and unexpected problems. Additional extensions will be granted only for serious medical issues with a written note from S^3 (for undergraduate students), the EECS graduate student office (38-444), or the MIT Office of Graduate Education. Late submissions not covered by a slack day will incur a penalty of 10% of the total available grade for each day of lateness. Note also that while we will endeavor to return graded work to you as soon as possible, if you use slack days you may miss a grading cycle and receive feedback in the following week.

Resubmitting Assignments. You may resubmit any assignment with a short (1 paragraph) summary of changes to potentially earn back 50% of the points lost in the original submission. Resubmissions must occur within 7 days of the original grades being released, and must use the same process as the initial submission. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

Class Participation

This course is mixes traditional lectures with more hands-on design exercises, interactive activities, and project presentations. Your class participation grade assesses your engagement across this spectrum of activites, and also considers your participation in posing and answering questions on the Piazza forum.

Reading Commentaries

Most lectures have one required and several optional readings associated with it. Lectures will assume that you have read, and are ready to discuss, the day's required reading. To facilitate the conversation, you are expected to submit a 1–2 paragraph commentary about each required reading on its nb page by noon on the day of the lecture. You may mark your commentary as "Anonymous to students" if you prefer. We will drop your two lowest commentary scores for the semester (e.g., you may choose to skip two readings without penalty).

Commentaries should not merely summarize the reading, but rather should exhibit one or more of the following:

• Critiques of arguments made in the paper.
• Analysis of implications or future directions for work discussed in lecture or readings.
• Clarification of some point or detail.
• Pose insightful questions, or answer other people's questions.

Acknowledgements

Material for this class has been adapted from classes taught by Jeffrey Heer at the University of Washington, Maneesh Agrawala at Stanford University, Hanspeter Pfister at Harvard University, Tamara Munzner at the University of British Columbia, Jessica Hullman and Nick Diakopoulos at Northwestern University, Niklas Elmqvist at the University of Maryland, College Park, Enrico Bertini at New York University, and Sheelagh Carpendale at Simon Fraser University. Thanks also to Michael Correll at Tableau Research. The class draws heavily on materials and examples found online, and we try our best to give credit by linking to the original source. Please contact us if you find materials where credit is missing or that you would rather have removed.

• Learning Goals
• Q&A ( Canvas )

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, visual art, 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 participating in class discussions, students will have to complete several short programming and data analysis assignments as well as a final project. Students will be expected to write up the results of the project in the form of a conference paper submission.

There are no prerequisites for the class and the class is open to graduate students as well as advanced undergraduates (by permission of instructor). Basic working knowledge of, or willingness to learn, graphics/visualization tools (e.g., D3, HTML5, OpenGL, etc) and data analysis tools (e.g., R, Excel, Matlab) will be useful.

Final Projects were be presented in the Paul G. Allen Center at the University of Washington on Tuesday June 7, 5-8pm . Public student final projects can be found at https://github.com/CSE512-16S .

• The Visual Display of Quantitative Information (2nd Edition). E. Tufte. Graphics Press, 2001.
• Envisioning Information , E. Tufte. Graphics Press, 1990.

Learning Goals & Objectives

• 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, networks, text and cartography.
• Practical experience building and evaluating visualization systems.
• The ability to read and discuss research papers from the visualization literature.

Schedule & Readings

• REQUIRED Chapter 1: Information Visualization, In Readings in Information Visualization. Stuart Card, Jock Mackinlay, and Ben Shneiderman. pdf
• Optional Decision to launch the Challenger, In Visual Explanations. E. Tufte. (See also critique of Tufte's argument )
• Optional The Value of Visualization. Jarke van Wijk. Visualization 2005 pdf
• Optional Graphs in Statistical Analysis. F. J. Anscombe. The American Statistician, Vol. 27, No. 1 (Feb., 1973), pp. 17-21 jstor
• 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.
• REQUIRED The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations, Shneiderman, Proc. IEEE Conference on Visual Languages, Boulder 1996. pdf
• Optional Levels of Measurement , Wikipedia.
• Optional The Structure of the Information Visualization Design Space. Stuart Card and Jock Mackinlay. InfoVis 97. pdf
• Optional On the theory of scales of measurement. S.S. Stevens. jstor
• REQUIRED Chapter 3: The Power of Representation, In Things That Make Us Smart. Norman. pdf
• REQUIRED Chapter 4: Data-Ink and Graphical Redesign, In The Visual Display of Quantitative Information. Tufte.
• REQUIRED Chapter 5: Chartjunk, In The Visual Display of Quantitative Information. Tufte.
• REQUIRED Chapter 6: Data-Ink Maximization and Graphical Design, In The Visual Display of Quantitative Information.
• Optional A Conversation with Jeff Heer, Martin Wattenberg, and Fernanda Viégas acm
• Optional The representation of numbers. Zhang and Norman. pdf
• REQUIRED Chapter 8: Data Density and Small Multiples, In The Visual Display of Quantitative Information. Tufte.
• REQUIRED Chapter 2: Macro/Micro Readings, In Envisioning Information. Tufte.
• REQUIRED Chapter 4: Small Multiples, In Envisioning Information. Tufte.
• Optional Low-Level Components of Analytic Activity in Information Visualization. Robert Amar, James Eagan, and John Stasko. InfoVis 2005 pdf
• Optional Exploratory Data Analysis , NIST Engineering Statistics Handbook
• Optional Exploratory Data Analysis , Wikipedia.
• REQUIRED Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Stolte, Tang, and Hanrahan. IEEE TVCG 2002. pdf
• REQUIRED Multidimensional detective. A. Inselberg. InfoVis 1997. pdf
• Optional Dynamic queries, starfield displays, and the path to Spotfire. Shneiderman. html
• Optional A Rank-by-Feature Framework for Interactive Exploration of Multidimensional Data. Seo and Shneiderman. Information Visualization 2005 pdf
• PAA A114 3:00pm - 4:20pm
• REQUIRED Perception in visualization. Healey html
• REQUIRED Graphical Perception: Theory, Experimentation and the Application to the Development of Graphical Models. William S. Cleveland, Robert McGill, J. Am. Stat. Assoc. 79:387, pp. 531-554, 1984. pdf
• REQUIRED Chapter 3: Layering and Separation, In Envisioning Information. Tufte.
• Optional Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. Jeffrey Heer, Michael Bostock. CHI 2010. html
• Optional Gestalt and composition. In Course #13, SIGGRAPH 2002. Durand. pdf 6-up pdf
• Optional The psychophysics of sensory function. S.S. Stevens. pdf
• REQUIRED D3 : Data-Driven Documents. Bostock, Ogievetsky & Heer. InfoVis 2011. html
• Optional Declarative Language Design for Interactive Visualization. Heer & Bostock. InfoVis 2010. html
• Optional Software Design Patterns for Information Visualization. Heer & Agrawala. InfoVis 2006. html
• Chapter 5-10, In Interactive Data Visualization for the Web, Murray. ( html )
• Optional d3's tutorial page and gallery , d3 resources
• REQUIRED Interactive Dynamics for Visual Analysis, Heer & Shneiderman. pdf
• REQUIRED Postmortem of an Example, Bertin. pdf
• Demos ggobi , Homefinder , zipdecode , Table lens , NameVoyager , LA Homicide Plot
• Video Classic systems on stat-graphics.org
• Optional Visual Queries for Finding Patterns in Time Series Data. Hochheiser & Shneiderman. pdf
• Optional Generalized Selection via Interactive Query Relaxation. Heer, Agrawala, Willett. CHI 2008. pdf
• Optional The Cognitive Coprocessor Architecture for Interactive User Interfaces. George Robertson, Stuart K. Card, and Jock D. Mackinlay. UIST 1989. pdf
• Optional Exploration of the Brain's White Matter Pathways with Dynamic Queries. Akers, Sherbondy, Mackenzie, Dougherty, Wandell. Visualization 2004. html
• REQUIRED Animated Transitions in Statistical Data Graphics. Heer & Robertson. IEEE InfoVis 2007. pdf
• REQUIRED Effectiveness of Animation in Trend Visualization. Robertson, Fernandez, Fisher, Lee, Stasko. InfoVis 2008. pdf
• Optional Animation: Can It Facilitate? Barbara Tversky, Julie Morrison, Mireille Betrancourt, International Journal of Human Computer Studies, v57, p247-262. 2002. pdf
• Optional Smooth and Efficient Zooming and Panning. Jack J. van Wijk and Wim A.A. Nuij. InfoVis 2003. pdf
• Optional Animated Exploration of Graphs with Radial Layout, Ping Yee, Danyel Fisher, Rachna Dhamija, and Marti Hearst. InfoVis 2001 pdf
• Optional Principles of Traditional Animation Applied to Computer Animation John Lasseter. Siggraph 1987. acm
• Optional Animation: From Cartoons to the User Interface. Bay-Wei Chang, David Ungar. UIST 1993. pdf
• REQUIRED Chapter 5: Color and Information, In Envisioning Information. Tufte.
• REQUIRED Brewer, Cynthia A., 1999, Color Use Guidelines for Data Representation, Proceedings of the Section on Statistical Graphics, American Statistical Association, Baltimore, pp. 55-60. html color scheme
• Optional Charting color from the eye of the beholder. Landa & Fairchild. html
• Optional ColorBrewer : Selecting good color schemes for maps. Cindy Brewer html
• Optional Color Naming Models for Color Selection, Image Editing and Palette Design. Heer & Stone. CHI 2012 html
• Optional BruceLindbloom : Useful color information, studies, and Files. Bruce Lindbloom. html
• Optional Meet iCam: A Next-Generation Color Appearance Model. CIC 2010. pdf html
• Optional Color2Gray: Salience-preserving color removal. Gooch, Olsen, Tumblin, Gooch. AMC Transactions on Graphics. pdf
• Optional A framework for transfer color based on the basic color categories. Chang, Saito, Naakajima. CGI 2003. pdf
• REQUIRED Arc Length-based Aspect Ratio Selection. Talbot, Gerth & Hanrahan. IEEE TVCG 2011. pdf
• REQUIRED Escaping Flatland. Envisioning Information. Tufte.
• REQUIRED Stacked Graphs – Geometry & Aesthetics. Byron & Wattenberg. InfoVis 08. pdf
• Links Nomograms
• Optional A Fisheye Follow-up. George Furnas. CHI 2006. acm
• Optional Space-scale diagrams: Understanding Multi-Scale Interfaces. Furnas and Bederson, CHI 1995. pdf
• Optional Guidelines for Using Multiple Views in Information Visualization. Wang et al. AVI 2000. acm
• Optional An Extension of Wilkinson’s Algorithm for Positioning Tick Labels on Axes. Talbot, Lin & Hanrahan. IEEE TVCG 2010. pdf
• Optional The Table Lens: Merging Graphical and Symbolic Representations in an Interactive Focus + Context Visualization for Tabular Information. Ramana Rao and Stuart K. Card. SIGCHI 1994. pdf
• Optional The Visual Design and Control of Trellis Display. Becker, Cleveland and Shyu. pdf
• REQUIRED Scalable, Versatile and Simple Constrained Graph Layout. Tim Dwyer. EuroVis 2009. pdf
• REQUIRED Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006. pdf
• Demos D3 Examples , Tree Layout in Flare (Click the "Layouts" link)
• Optional ManyNets: An Interface for Multiple Network Analysis and Visualization. Freire et al. CHI 2010. pdf
• Optional Graph Visualization and Navigation in Information Visualization: A Survey, Herman, Melancon, and Marshall, IEEE TVCG 2000. pdf (Skimming to get the overview is sufficient)
• Optional Visual Exploration of Multivariate Graphs. Wattenberg. CHI 2006. pdf
• Optional A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations. Ghoniem, Fekete, Castagliola. InfoVis 2004. pdf
• Optional Dig-cola: Directed graph layout through constrained energy minimization. Dwyer and Koren. InfoVis 2005. pdf
• Optional Vizster: Visualizing Online Social Networks. Heer & boyd. InfoVis 2005. pdf
• Optional Interactive Visualization of Genealogical Graphs. Michael J. McGuffin and Ravin Balakrishnan. InfoVis 2005. pdf
• Optional A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies. Lamping, Rao, Pirolli. CHI 1995. html
• REQUIRED Chapter 11: The Cartogram: Value-by-Area Mapping. In Cartography: Thematic Map Design. Dent pdf
• REQUIRED Adaptive Composite Map Projections. Bernhard Jenny. InfoVis 2012. pdf
• Links Map Projections , Cartogram Central , Myriahedral Projections
• REQUIRED Information Visualization for Search Interfaces, Marti Hearst, ''Search User Interfaces'', Chapter 10 html
• REQUIRED Information Visualization for Text Analysis, Marti Hearst, ''Search User Interfaces'', Chapter 11 html
• REQUIRED Interpretation and Trust: Designing Model-Driven Visualizations for Text Analysis. Chuang et al. CHI 2012 html
• Optional Mapping Text with Phrase Nets. Frank van Ham, Martin Wattenberg, Fernanda B. Viégas. InfoVis 2009. pdf
• Optional Termite: Visualization Techniques for Assessing Textual Topic Models. Chuang et al. AVI 2012 html
• REQUIRED (Reading) pdf
• Optional (Reading) pdf
• REQUIRED 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. html
• REQUIRED Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. Michael Correll, Michael Gleicher. IEEE InfoVis 2014 . pdf
• Optional Visual Semiotics & Uncertainty Visualization: An Empirical Study. Alan MacEachren, Robert Roth, James O'Brien, Bonan Li, Derek Swingley, Mark Gahegan. IEEE InfoVis 2012 . html
• Optional The Visual Communication of Risk. Isaac Lipkus, J.G. Hollands. Journal of the National Cancer Institute. pdf
• REQUIRED Narrative Visualization: Telling Stories with Data. Segel & Heer. InfoVis 2010. html
• REQUIRED A Deeper Understanding of Sequence in Narrative Visualization. Jessica Hullman, Steven Drucker, Nathalie Henry Riche, Bongshin Lee, Danyel Fisher, and Eytan Adar. InfoVis 2013. pdf
• Optional Visualization Rhetoric: Framing Effects in Narrative Visualization. Hullman & Diakopoulos. InfoVis 2011. pdf
• Optional Budget Forecasts, Compared With Reality. NYTimes, February 2010. html
• Optional How Mariano Rivera Dominates Hitters. NYTimes, June 2010. html
• Optional Gapminder Human Development Trends 2005. html
• REQUIRED Voyagers and Voyeurs: Supporting Asynchronous Collaborative Information Visualization. Heer, Viégas, & Wattenberg. CHI 2007. html
• REQUIRED Designing for Social Data Analysis. Wattenberg and Kriss. InfoVis 2006. pdf
• Optional Design Considerations for Collaborative Visual Analytics. Heer and Agrawala. InfoVis 2008. html
• Optional Strategies for Crowdsourcing Social Data Analysis. Willett et al. CHI 2012. html
• Optional CommentSpace: Structured Support for Collaborative Visual Analysis. Willett et al. CHI 2011. html
• Optional Many Eyes: A Site for Visualization at Internet Scale. Viégas et al. InfoVis 2007. pdf
• REQUIRED A Nested Model for Visualization Design and Validation. Tamara Munzner. InfoVis 2009 pdf
• Optional Design Study Methodology: Reflections from the Trenches and the Stacks. Sedlmair et al. Infovis 2012 html
• Optional The Challenge of Information Visualization Evaluation. Catherine Plaisant. AVI 2004 pdf

Finals Week

• Paul G. Allen Center Atrium, 5-8pm

Assignments

• Class Participation 10%
• Assignment 1: Visualization Design 10%
• Assignment 2: Exploratory Data Analysis 15%
• Assignment 3: Interactive Visualization 25%
• Final Project 40%

Class Participation

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.

Class participation includes both in-class participation as well as participation in the discussion on Canvas . All enrolled students are required to post at least 1 substantive discussion reply pertaining to each lecture and set of readings by 1 day after class (11am on the day after each class). Each student has 2 passes for skipping comments. Links to the Canvas discussion for each lecture will be posted on the schedule above.

• 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

Late Policy: We will deduct 10% for each day (including weekends and holidays) an assignment is late.

Plagiarism Policy: Assignments should consist primarily of your 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.

Please feel free to peruse the resources page.

Questions should be posted on Canvas . If you have a private question, email the instructors at cse512@cs or come to office hours.