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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 official prerequisites for the class, but the class is aimed at graduate students and advanced undergraduates. However, familiarity with the material in CS147, CS 148 and CS142 can be useful. Even more important is a basic working knowledge of, of some web-programming and/or graphics API (e.g. Javascript/D3, Python, WebGL). Experience with data analysis applications (e.g. Excel, Matlab, R) is also helpful. 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.



Week 1

M Sep 26: The Purpose of Visualization [ Readings | Submit Response | Slides ]

Assigned: Assignment 1 (due Oct 2 by 11:59pm)

W Sep 28: Data and Image Models [ Readings | Submit Response | Slides ]

Week 2

M Oct 3: Visualization Design [ Readings | Submit Response | Slides ]

Due (by 11:59pm Oct 2): Assignment 1

W Oct 5: Exploratory Data Analysis [ Readings | Submit Response | Slides ]

Assigned: Assignment 2 (due Oct 17 before class)

Week 3

M Oct 10: Perception [ Readings | Submit Response | Slides ]

W Oct 12: Interaction [ Readings | Submit Response | Slides ]

Week 4

M Oct 17: Introduction to D3 [ Readings | Submit Response | D3v4 Slides | D3v3 Slides ]

Due (before class): Assignment 2
Assigned: Assignment 3 (due date Oct 31 before class)

W Oct 19: D3 Tutorial [ Readings | Submit Response | Slides | Solutions ]

Week 5

M Oct 24: Interaction II [ Readings | Submit Response | Slides ]

W Oct 26: Color [ Readings | Submit Response | Slides ]

Week 6

M Oct 31: Using Space Effectively: 2D [ Readings | Submit Response | Slides ]

Due: Assignment 3
Assigned: Final Project (project proposal due Nov 7 before class)

W Nov 2: Spatial Layout [ Readings | Submit Response | Slides ]

Week 7

M Nov 7: Identifying Design Principles [ Readings | Submit Response | Slides ]

Due: Final Project (project proposal)

W Nov 9: Deconstructing Visualizations [ Readings | Submit Response | Slides ]

Week 8

M Nov 14: Collaborative Visual Analysis [ Readings | Submit Response | Slides ]

W Nov 16: Project Progress Presentations

Due (by 10am): Final Project (project progress presentation slides)

Thanksgiving Week

M Nov 21: No class due to Thanksgiving

W Nov 23: No class due to Thanksgiving

Week 9

M Nov 28: Graph Layout [ Readings | Submit Response | Slides ]

W Nov 30: Network Analysis [ Readings | Submit Response | Slides ]

Week 10

M Dec 5:Text Visualization [ Readings | Submit Response | Slides ]

W Dec 7: Animation [ Readings | Submit Response | Slides ]

F Dec 9 Time and Location: Fri 12/9 3pm-5pm at Lathrop 282 Final Project Presentations


Course Numbers: CS448B Visualization
Instructor: Maneesh Agrawala
Course Assistants: Ludwig Schubert, Peter Washington
Meeting: 420-41 Jordan Hall, MW 1:30-3pm

Office Hours:

  • Maneesh Agrawala: 364 Gates, Mon: 3-4pm and by appointment
  • Ludwig Schubert: Huang Engineering Center Basement, Sun 6-7pm and by appointment
  • Peter Washington: Lathrop Tech Lounge, Tue 8-9pm and by appointment

To contact us please use Piazza. This is the fastest way to get a response.


Your best bet is to order them online.
Please order soon. Readings will be assigned in the first week of class.


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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