ECS 163 — Information Interfaces

The ability to effectively explore, analyze, and explain data is paramount in the era of computing and big data. This course will delve into the art and science of information visualization and interface design, tailored for diverse and complex data landscapes encountered in daily activities. The lectures will cover design principles of human-computer interaction, offer a comprehensive exploration of data visualization techniques, and provide authentic practices (e.g., in-class activities, homework assignments, and course projects) for implementing and evaluating interactive visualization systems. The goal is to empower students with the skills to combat information overload and engage a broader audience in analytic thinking through compelling visual representations.

Intended Learning Objectives

By the end of this course, you are expected to:

1. Gain a comprehensive understanding of fundamental data visualization techniques and theories.
2. Develop the ability to create, assess, and provide constructive criticism for various visualization designs.
3. Acquire hands-on experience in building interactive data visualizations using D3.js.
4. Harness your skills to create a visualization dashboard for explorative data analysis or compelling storytelling.
5. Engage with current research in the field of visualization through reading and discussing visualization research papers.

Lecture Information

Lecture Time: 3:10 - 4:00 PM Monday, Wednesday and Friday

Location: Young Hall 184

Instructor: Dongyu Liu, 2123 Kemper

Instructor Office Hours: Wednesday 11AM - Noon (location: Kemper 2123)

Teaching Assistant: Ying-Cheng (Jessica) Chen (ycjchen@ucdavis.edu)

TA Office Hours: Friday 10AM - Noon (location: Kemper 3106)

GitHub Repository for Programming Assignments https://github.com/via-teaching/ecs163-24w

Discussion Piazza (Canvas Integrated) We use Piazza for Q&A as it is more formal and well-organized. Our course staff will constantly check it. We encourage active participation by posting and answering questions on Piazza. If you're exceptionally helpful to other students, we'll consider you for extra credit.

Textbook: There is no textbook, but you may find these two books helpful for this class:

1. Visualization Analysis and Design, Tamara Munzner, A K Peters/CRC Press, December 2014 (UC e-Resources)
2. Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O’Reilly Press. (UC e-Resources)

Additional Tutorials: This resources page has a lot of useful learning materials.

Grading Breakdown:

    HW Assignment 0            3%

    HW Assignment 1            5%

    HW Assignment 2          15%

    HW Assignment 3          20%

    Reading Reports             14%

    Final Project                    35%

    Class Participation            8%

    (Workshops + Final Proposal Presentations)

Schedule

Week 1: January 8

Mon 1/8	Lecture: Introduction + Course Overview REQUIRED Chapter 1 What's Vis, and Why Do it? (page 1-20), Visualization Analysis & Design, Tamara Munzner, CRC Press, 2014 [UC e-resources] REQUIRED Views on Visualization. J.J. van Wijk. IEEE TVCG 12(4), p. 421-432, 2006 [pdf] [Optional] Chapter 1: Information Visualization. Stuart Card, Jock Mackinlay, and Ben Shneiderman. Readings in Information Visualization. 1999. [pdf]
Tue 1/9	No Discussion
Wed 1/10	Lecture: Introduction to Data Visualization
Fri 1/12	Lecture: Data, Marks, and Channels I ASSIGNED HW0 (Sketching Visualizations)

Week 2: January 15

Mon 1/15	No lecture due to Martin Luther King Jr. Day DUE Reading Report 1 (11:55am) REQUIRED The Science of Visual Data Communication: What Works, S. Franconeri et al., Psychological Science in the public interest 2021 [pdf ] (pages 1 -- 15 , stop at the section titled "How to design a perceptually efficient vis")
Tue 1/16	Discussion: Visualization Programming Overview + Reading Discussion (Jessica)
Wed 1/17	Workshop: Web (HTML/CSS/JS) Programming (Jessica)
Fri 1/19	Lecture: Data, Marks, and Channels II DUE HW0 (11:55pm) ASSIGNED HW1 (Static Visualizations)

Week 3: January 22

Mon 1/22	Workshop: Introduction to D3.js (Jessica) DUE Reading Report 2 (11:55am) REQUIRED The Science of Visual Data Communication: What Works, S. Franconeri et al., Psychological Science in the public interest 2021 [pdf ] (pages 15-End) OR Evaluating Interactive Graphical Encodings for Data Visualization. Bahador Saket et al. IEEE Transactions on Visualization and Graphics 24(3):1316-1330  (2018) [pdf]  [Optional] Chapters 2 and 5 of Visualization Analysis & Design, Tamara Munzner, CRC Press, 2014 [UC e-resources]
Tue 1/23	Discussion: HW1 Details + Reading Discussion (Jessica)
Wed 1/24	Lecture: Effective Visual Encoding I
Fri 1/26	Lecture: Effective Visual Encoding II ASSIGNED HW2 (Visualization Dashboard: Layout Design + Visual Encoding)

Week 4: January 29 

Mon 1/29	Lecture: Graphical Perception I DUE Reading Report 3 (11:55am) DUE HW1 (11:55pm) REQUIRED Mapping Color to Meaning in Colormap Data Visualizations. Karen B. Schloss et al., In Proceedings of InfoVis 2018 [pdf]  [Optional] Chapters 6 and 10 of Visualization Analysis & Design, Tamara Munzner, CRC Press, 2014 [UC e-resources] [Optional] Perception in Visualization. Christopher G. Healey [link]
Tue 1/30	Discussion: HW2 Details + Reading Discussion (Jessica)
Wed 1/31	Lecture: Graphical Perception II + Color I
Fri 2/2	Lecture: Color II

Week 5: February 5

Mon 2/5	Lecture: Interaction Design DUE Reading Report 4 (11:55am) DUE Final Project Team (11:55pm) REQUIRED Subjectivity in personal storytelling with visualization. Information Design Journal. S. Carpendale, A. Thudt, C. Perin, and W. Willett.  IEEE Transactions on Visualization and Computer Graphics 23(1):48-64 (2017). [pdf ] OR More Than Telling a Story: Transforming Data into Visually Shared Stories. B. Lee, N. H. Riche, P. Isenberg and S. Carpendale. IEEE Computer Graphics and Applications 35(5):84-90 (2015) [pdf] [Optional] Chapters 10  of Information Visualization: Perception for Design, Colin Ware, Morgan Kaufmann, 2021 [UC e-resources] [Optional] Chapters 11 and 13 of Visualization Analysis & Design, Tamara Munzner, CRC Press, 2014 [UC e-resources] [Optional] The death of interactive infographics? [link]
Tue 2/6	Discussion: Final project details, examples, etc. (Jessica)
Wed 2/7	Lecture: Animations
Fri 2/9	Lecture: Narrative Visualization DUE HW2 (11:55pm) ASSIGNED HW3 (Visualization Dashboard: Interactivity)

Week 6: February 12

Mon 2/12	Workshop: D3 Interactions & Animations DUE Reading Report 5 (11:55am)
Tue 2/13	Discussion (Jessica)
Wed 2/14	Final Project Proposal Presentations
Fri 2/16	Final Project Proposal Presentations

Week 7: February 19

Mon 2/19	No lecture due to Presidents' Day REQUIRED The shape of Information by Visual Metaphors. C. Ziemkiewicz and R. Kosara. IEEE Transactions on Visualization and Computer Graphics 14(6):1269-76 (2008) [pdf]
Tue 2/20	Discussion: (Jessica)
Wed 2/21	Lecture: Multivariate Tables I
Fri 2/23	Lecture: Multivariate Tables II DUE HW3 (11:55pm) DUE Final Project Proposal (11:55pm)

Week 8: February 26 

Mon 2/26	Lecture: Mapping & Cartography DUE Reading Report 6 (11:55am) REQUIRED A Nested Model for Visualization Design and Validation. Tamara Munzner. In Proceedings of InfoVis 2009. [pdf]
Tue 2/27	Discussion: (Jessica)
Wed 2/28	Lecture: Evaluation I
Fri 3/1	Lecture: Evaluation II

Week 9: March 4

Mon 3/4	Lecture: Graph and Network Visualization DUE Reading Report 7 (11:55am)
Tue 3/5	Discussion (Jessica)
Wed 3/6	Lecture: Time-Oriented Data I
Fri 3/8	Lecture: Time-Oriented Data II

Week 10: March 11

Mon 3/11	Lecture: Visual Analytics
Tue 3/12	No Discussion
Wed 3/13	Lecture: eXplainable Artificial Intelligence
Fri 3/15	Final Project Presentations (2pm-5pm) DUE Final project including source code and report  (10:00am)

Week 11: March 18

Mon 3/18

Final Project Presentations (2pm-5pm)