WeekDateTopic (lecture)DateTopic (lab)
1 8/27 Intro / Administrivia 8/29 Introduction
2 9/3 Intro / History 9/5 HTML
3 9/10 Lilly Library visit 9/12 CSS
4 9/17 Guest lecture: Katy Börner 9/19 Javascript - 1 / A#1
5 9/24 Perception 9/26 Javascript - 2 / A#2
6 10/1 Design 10/3 SVG / A#3
7 10/8 Data / proposal due 10/10 D3
8 10/15 Fundamental Visualizations 10/17 Drawing with data / A#4
9 10/22 Fundmeantal visualizations - 2 10/24 Drawing with Data - 2 / A#5
10 10/29 Texts / Project update due 10/31 Texts
11 11/5 Maps - 1 11/7 Maps - 1
12 11/12 Maps - 2 / A#6 11/14 Maps - 2
13 11/19 Graphs and Trees - 1 / Project update due 11/21 Graphs and Trees - 1
14 11/26 Thanksgiving 11/28 Thanksgiving
15 12/3 Graphs and Trees - 2 12/5 Graphs and Trees - 2
16 12/10 Final project presentation 12/12 Final project presentation


Time & Location
Tuesday 4pm-5:15pm, Informatics West 107 (Lecture)
Thursday 4pm-5:15pm, Informatics West 109 (Lab)

First meeting: Tuesday, Aug. 27th, 2013
Yong-Yeol Ahn (YY)
Office: Informatics East Room 316
Phone: (812) 856 2920
Office hours: Wednesday 3pm-5pm, or by appointment (email me or use MeetMe)
Vikas Rao Pejaver
Office hours: Tuesday 10am-11am (Lindley Hall LH 310), Thursday 5:15pm-6:15pm (Info West I 109)
Matthew O. Ward, Georges Grinstein, and Daniel Kim, Interactive Data Visualization: Foundations, Techniques, and Applications [Amazon]
Scott Murray, Interactive Data Visualization for the Web [Amazon]
We will learn basics of Javascript and D3.js to create visualizations and play with them.

This course is open to graduate students as well as advanced undergraduate students. There is no formal requirements, but it is recommended to have programming background (I210 & I211 or equivalent). Also I308: "Information Representation" is a recommended class before taking this class. Working knowledge of Javascript and scripting languages (e.g. Python) will be helpful. Contact the instructor if you are uncertain about your background.


From car dashboards to cutting-edge scientific papers, we extensively use visual representation of data. Data visualization is becoming a crucial skill for knowledge workers, particularly in the era of big data, because visualization is critical to understand big, messy data. In this course we will learn fundamentals of data visualization and create data visualizations using various types of data.


By the end of the course, you will be able to evaluate data visualizations based on the understanding of the principles of visualization, various types of data, and the strengths and limitations of an array of visualization techniques. You will be able to interpret complex datasets by applying appropriate visualization techniques.

Final assessement

For the final assessment, you will choose one of two options: design & implementation or analysis. In the design & implementation project, you will pick a real visualization problem and design & implement a new visualization. You will write a final paper reporting the background, implementation details, analysis and evaluation of their solution to the problem. In the analysis project, you will choose a dataset and analyze it through various visualization techniques. You will write a final paper that provides the data description, analysis and evaluation of used techniques.

Class policies

Academic integrity

The principles of academic honesty and ethics will be enforced. Any cases of academic misconduct (cheating, fabrication, plagiarism, etc) will be thoroughly investigated and immediately reported to the School and the Dean of Students. You should actively discuss with others, but you should write your own report. Credit all the sources (discussion with other students, used softwares, etc).

Grading policy



The deliverables are

Project proposal (Due: 10/8)

An one or two page document that contains

Proposal presentation (10/8-10/15)

We will follow the Ignite format. You should have 20 slides and each slide will auto-advance every 15 seconds. You should submit the slides the day before the class. It should address

Progress report (#1 Due: 10/29, #2 Due: 11/19)

Final report (Due: 12/17)

Final presentation (12/10, 12/12)

Again we will follow the Ignite format. but this time 10 minues for each team. You should have 40 slides and each slide will auto-advance every 15 seconds. You should submit the slides by the night of 4/23.




Softwares, libraries, and data


Network visualization



See YYiki: Information visualization books