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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. 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. Send me (Maneesh) email if you are worried about whether you have the background for the course.

Announcements

  • Check out the Visualization Gallery and add any interesting visualizations you find on that page.
  • The first time you login to this wiki it will automatically create an account for you. After logging in add yourself to the list of Participants page.
  • New Visualizations posted to the Visualization Gallery.

Schedule

Week 1

W Sep 3: The Purpose of Visualization [ Readings | Submit Reading Response | Slides ]

Assigned: Assignment 1 (due Sep 9 by midnight)


Week 2

M Sep 8: Data and Image Models [ Readings | Submit Reading Response | Slides ]

W Sep 10: Visualization Design [ Readings | Submit Reading Response | Slides ]

Due (by midnight Sep 9): Assignment 1
Assigned: Assignment 2 (due Sep 29 before class)


Week 3

M Sep 15: Exploratory Data Analysis [ Readings | Submit Reading Response | Slides ]

W Sep 17: Multidimensional Data Visualization [ Readings | Submit Reading Response | Slides ]


Week 4

M Sep 22: Perception [ Readings | Submit Reading Response | Slides ]

W Sep 24: Interaction [ Readings | Submit Reading Response | Slides ]


Week 5

M Sep 29: Interaction II [ Readings | Submit Reading Response | Slides ]

Due (before class): Assignment 2
Assigned: Assignment 3 (due Oct 15 before class)

W Oct 1: Color [ Readings | Submit Reading Response | Slides ]


Week 6

M Oct 6: Introduction to D3 [ Readings | Submit Reading Response | Slides ]

W Oct 8: Wrangling Data (guest lecture by Joe Hellerstein) [ Readings | Submit Reading Response | Slides ]


Week 7

M Oct 13: Using Space Effectively: 2D [ Readings | Submit Reading Response | Slides ]

W Oct 15: Spatial Layout [ Readings | Submit Reading Response | Slides ]

Due: Assignment 3
Assigned: Final Project (project proposal due Oct 27 before class)


Week 8

M Oct 20: Identifying Design Principles [ Readings | Submit Reading Response | Slides ]

W Oct 22: Network Analysis and Visualization [ Readings | Submit Reading Response | Slides ]


Week 9

M Oct 27: Identifying Design Principles II [ Readings | Submit Reading Response | Slides ]

Due: Final Project (project proposal)

W Oct 29: Graph Layout [ Readings | Submit Reading Response | Slides ]


Week 10

M Nov 3: Collaborative Visual Analysis [ Readings | Submit Reading Response | Slides ]

W Nov 5: Crowdsourcing Visual Analysis [ Readings | Submit Reading Response | Slides ]


Week 11

M Nov 10: In Class Project Presentations

W Nov 12: In Class Project Presentations


Week 12

M Nov 17: Text Visualization [ Readings | Submit Reading Response | Slides ]

W Nov 19: Deconstructing Visualizations [ Readings | Submit Reading Response | Slides ]


Week 13

M Nov 24: Storytelling [ Readings | Submit Reading Response | Slides ]

W Nov 26: No class


Week 14

M Dec 1: Animation [ Readings | Submit Reading Response | Slides ]

W Dec 3: Final Poster Session - 1-2:30pm, 5th Floor Soda Hall, Open to the Public


Information

Course Numbers: CS294-10 Visualization
Units: 1 unit S/U or 3 units for a letter grade
Instructors: Maneesh Agrawala (maneesh at cs.berkeley.edu) and Jessica Hullman (jhullman at berkeley.edu)
Meeting: 310 Soda Hall, MW 1-2:30pm

Office Hours:

  • Maneesh: 535 Soda Hall, Mon: 2:30-4pm and by appointment
  • Jessica: 523 Soda Hall, Fri: 2:30-4pm and by appointment

Textbooks:

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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Credits

Tableau's software is provided through the Tableau for Teaching program.