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Visual media are increasingly generated, manipulated, and transmitted by computers. When well designed, such displays capitalize on human facilities for processing visual information and thereby improve comprehension, memory, inference, and decision making. Yet the digital tools for transforming data into visualizations still require low-level interaction by skilled human designers. As a result, producing effective visualizations can take hours or days and consume considerable human effort.
In this course we will study techniques and algorithms for creating effective visualizations based on principles and techniques 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. The class will meet twice a week. In addition to participating in class discussions, students will have to complete several short programming and data analysis assignments as well as a final programming 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, but the class is aimed at graduate students as well as advanced undergraduates. A basic working knowledge of, or willingness to learn, a graphics API (e.g. Javascript/D3, Python, WebGL) and applications (e.g. Excel, Matlab) will be useful. The final project can be developed using any suitable language or application. While we will cover a little bit of Javascript/D3 in class, most of the other APIs, applications and languages will not be taught in the course. However many introductory tutorials at the level required for the class are available on the web and we can help you find the relevant information as you need it. Contact me (Maneesh) via Piazza if you are worried about whether you have the background for the course.
Announcements
- Office hour changes on week 7: Scott's Wednesday office hour is moved to Monday 5/9 4-5pm in Lathrop Tech Lounge. Peter's Thursday office hour is moved to Friday 5/13 5-6pm in Gates Basement.
- As mentioned on Piazza, we have extra office hours for D3: 1pm-2pm Friday 4/22 @ Gates 5th Floor (Scott), 5pm-6pm Sunday 4/24 @ Lathrop Tech Lounge (Peter), 3pm-4pm Monday 4/25 @ Gates 5th Floor (Ludwig). We will also be hosting a hands-on D3 workshop 8pm-10pm Tuesday 4/26 @ Gates 104.
- As decided by vote in class today (Mon Mar 28) if you'd like to use your laptop during lecture, please sit in the back two rows of the classroom.
Schedule
Week 1
M Mar 28: The Purpose of Visualization [ Readings | Submit Reading Response | Slides ]
- Assigned: Assignment 1 (due Apr 3 by 11:59pm)
W Mar 30: Data and Image Models [ Readings | Submit Reading Response | Slides ]
Week 2
M Apr 4: Visualization Design [ Readings | Submit Reading Response | Slides ]
- Due (by 11:59pm Apr 3): Assignment 1
W Apr 6: Exploratory Data Analysis [ Readings | Submit Reading Response | Slides ]
- Assigned: Assignment 2 (due Apr 18 before class)
Week 3
M Apr 11: Multidimensional Data Visualization [ Readings | Submit Reading Response | Slides ]
W Apr 13: Perception [ Readings | Submit Reading Response | Slides ]
Week 4
M Apr 18: Introduction to D3 [ Readings | Submit Reading Response | Slides ]
- Due (before class): Assignment 2
- Assigned: Assignment 3 (due May 4 before class)
W Apr 20: Interaction [ Readings | Submit Reading Response | Slides ]
Week 5
M Apr 25: Interaction II [ Readings | Submit Reading Response | Slides ]
W Apr 27: Color [ Readings | Submit Reading Response | Slides ]
Week 6
M May 2: Using Space Effectively: 2D [ Readings | Submit Reading Response | Slides ]
W May 4: Spatial Layout [ Readings | Submit Reading Response | Slides ]
- Due: Assignment 3
- Assigned: Final Project (project proposal due May 11 before class)
Week 7
M May 9: Identifying Design Principles [ Readings | Submit Reading Response | Slides ]
W May 11: Deconstructing Visualizations [ Readings | Submit Reading Response | Slides ]
- Due: Final Project (project proposal)
Week 8
M May 16: Collaborative Visual Analysis [ Readings | Submit Reading Response | Slides ]
W May 18: Graph Layout [ Readings | Submit Reading Response | Slides ]
Week 9
M May 23: Project Progress Presentations
- Due (by 10am): Final Project (project progress presentation slides)
- Presentation Logistics
- Group A - Meet in Herrin T175:
- Adrian Leven - Submit Feedback
- Alex Engel - Submit Feedback
- Andrew McCabe, Pascal Odek, Juan... - Submit Feedback
- April Yu, Kirby Gee, and Gavin Mai - Submit Feedback
- Brad Reyes and Mark Schramm - Submit Feedback
- Catherine Mullings, Ben-han Sung, Andrei Terentiev - Submit Feedback
- Cathy Zhu and Lucas Throckmorton - Submit Feedback
- Chinmayi Dixit and Filippa Karrfelt - Submit Feedback
- Tum Chaturapruek - Submit Feedback
- Dan Guo - Submit Feedback
- Ella Kim and Leah Kim - Submit Feedback
- Emma Townley-Smith, Sarah Sterman and Juliana Cook - Submit Feedback
- Erin Singer, Patrick Briggs, and John Newcomb - Submit Feedback
- Ian Proulx, John Reyna, Kush Nijhawan - Submit Feedback
- Flavia Grey and Maria Frank - Submit Feedback
- Gabriella Brignardello, John Morgan, and Milan Doshi - Submit Feedback
- Gloria Chua and Janette Cheng - Submit Feedback
- Jennifer Lu and Christina Kao - Submit Feedback
- Joanne Jang and Zheng Wu - Submit Feedback
- Kat Phan, Brandon Liu, Christine Quan - Submit Feedback
- Group A - Meet in Herrin T175:
- Group B - Meet in Gates 219:
- Kris Sankaran - Submit Feedback
- Kyle Dumovic - Submit Feedback
- Lawrence Rogers - Submit Feedback
- Leigh Hagestad and Sarah Nader - Submit Feedback
- Lorena Huang-Liu, Haiyin Wang, and Serena Wong - Submit Feedback
- Mackenzie Leake - Submit Feedback
- Matthew Pick and Ashley Ngu - Submit Feedback
- Mimi Yang and Benjamin Wang - Submit Feedback
- Nikolas Martelaro - Submit Feedback
- Oskar Ankarberg and Pontus Orraryd - Submit Feedback
- Sage Isabella - Submit Feedback
- Samuel Hansen and Shirbi Ish-Shalom - Submit Feedback
- Sanby Lee - Submit Feedback
- Sarah Wymer - Submit Feedback
- Shannon Kao - Submit Feedback
- Shenli Yuan and Ernesto Ramirez - Submit Feedback
- Steve Kim and Jed Tan - Submit Feedback
- Sukhi Gulati, Raymond Luong, and Julie Ni - Submit Feedback
- Cong Qiaoben - Submit Feedback
- Zach Maurer and Santiago Seira - Submit Feedback
- Group B - Meet in Gates 219:
W May 25: Network Analysis [ Readings | Submit Reading Response | Slides ]
Week 10
M May 30: No class due to Memorial Day Holiday
W Jun 1: Animation [ Readings | Submit Reading Response | Slides ]
F Jun 3 12:15-2:15pm, Lathrop 282: Final Project Presentations
Information
Course Numbers: CS448B Visualization
Instructor: Maneesh Agrawala
Course Assistants: Scott Cheng, Ludwig Schubert, Peter Washington
Meeting: T175 Herrin Hall, MW 1:30-3pm
Office Hours:
- Maneesh Agrawala: 364 Gates, Mon: 3-4pm and by appointment
- Scott Cheng: 5th Floor Gates, Wed: 11am-12pm and by appointment
- Ludwig Schubert: 5th Floor Gates, Tue: 9-10am and by appointment
- Peter Washington: Gates Basement, Thu: 7-8pm and by appointment
To contact us please use Piazza. This is the fastest way to get a response.
Textbooks:
- The Visual Display of Quantitative Information (2nd Edition). E. Tufte. Graphics Press, 2001.
- Envisioning Information (2nd Edition), E. Tufte. Graphics Press, 2005.
Your best bet is to order them online.
Please order soon. Readings will be assigned in the first week of class.
Requirements
Class participation (10%)
Assignment 1: Visualization Design (10%)
Assignment 2: Exploratory Data Analysis (15%)
Assignment 3: Creating Interactive Visualization Software (25%)
Final Project (40%)
Late Policy: For assignments we will deduct 10% for each day (including weekends) the 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.
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