CS294-10 Visualization

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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 and the class is open to graduate students as well as advanced undergraduates. However, a basic working knowledge of, or willingness to learn, a graphics API (e.g. GDI+, OpenGL, Java2D, Flash/Flex) and applications (e.g. Excel, Matlab) will be useful. The final project can be developed using any suitable language or application. While these APIs, applications and languages will not be taught in class, many introductory tutorials at the level required for the class are available on the web. Send me (Maneesh) email if you are worried about whether you have the background for the course.




Jan 19: The Purpose of Visualization [ Readings | Slides ]

Assigned: Assignment 1 (due Jan 26 by 9am)

Jan 24: Data and Image Models [ Readings | Slides ]

Jan 26: Visualization Design [ Readings | Slides ]

Due (by 9am): Assignment 1

Jan 31: No class

Feb 2: Exploratory Data Analysis [ Readings | Slides ]

Assigned: Assignment 2 (due Feb 14 before class)

Feb 7: Multidimensional Data Visualization [ Readings | Slides ]

Feb 9: Perception [ Readings | Slides ]

Feb 14: Interaction [ Readings | Slides ]

Due: Assignment 2
Assigned: Assignment 3 (due Mar 7 before class)

Feb 16: Interaction II [ Readings | Slides ]

Feb 21: President's Day - No Lecture

Feb 23: Color [ Readings | Slides ]

Feb 28: Protovis (guest lecture from Jeff Heer)[ Readings | Slides ]

Mar 2: Collaborative Visual Analysis (guest lecture from Wesley Willett) [ Readings | Slides ]

Mar 7: Using Space Effectively: 2D [ Readings | Slides ]

Due: Assignment 3
Assigned: Final Project (project proposal due Mar 14 before class)

Mar 9: Using Space Effectively: 2D II [ Readings | Slides ]

Mar 14: Spatial Layout [ Readings | Slides ]

Due: Final Project (project proposal)

Mar 16: Identifying Design Principles [ Readings | Slides ]

Mar 21: Spring Break

Mar 23: Spring Break

Mar 28: Using Space Effectively: 3D [ Readings | Slides ]

Mar 30: Conveying Structure [ Readings | Slides ]

Apr 4: Initial Project Presentations I

Apr 6: Initial Project Presentations II

Apr 11: Graph Layout [ Readings | Slides ]

Apr 13: Graph Layout II [ Readings | Slides ]

Apr 18: Text Visualization [ Readings | Slides ]

Apr 20: Conveying Shape: Lines [ Readings | Slides ]

Apr 25: Conveying Shape: Lighting, Shading, Texture [ Readings | Slides ]

Apr 27: Animation [ Readings | Slides ]

May 3: Final Poster Presentations 1:30-3pm 6th Floor Atrium Soda Hall


Course Numbers: CS294-10
Instructor: Maneesh Agrawala (maneesh at cs.berkeley.edu)
Meeting: 405 Soda Hall, Mon-Wed 1-2:30pm

Office Hours:

  • Maneesh: 635 Soda Hall, MW: 2:30-3pm and by appointment


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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