From a dashboard in your car to cutting-edge scientific papers, we extensively use visual representation of data. Thanks to the increasing amount of valuable data in every corner of our society, the visualization industry is growing rapidly and the visual analytics is becoming a crucial skill for knowledge workers. Effective analysis of data through visualization will become more and more crucial because it is almost impossible to understand big, messy data without any visual aid.

Why is visualization so powerful? Why some visualizations are more effective than others? What are the existing visualization methods? What are the current challenges in visualization?

In this course we will explore these questions by looking back the history of visualization, by analyzing and criticizing existing visualizations, and by creating our own visualization with data from our everyday life or your research problems. This course is both for the students who want to apply visualization techniques to their own work and for the students who want to develop better visualization algorithms and techniques.


Basic information

Time & Location
Informatics East 130
Tuesday & Thursday 9:30am–10:45am
First meeting: Jan. 10th, 2012
Yong-Yeol Ahn (YY)
Office: Informatics East Room 316
Phone: (812) 856 2920
Office hours: Tuesday & Thursday 1pm–2:30pm, or by appointment
(you can email me or use MeetMe:
Abhik Seal
Office: Lindley Hall Room 305
All announcements will be sent through a mailing list (a Google group).
Edward R. Tufte, The Visual Display of Quantitative Information (2nd Eds.)
This course is open to graduate students as well as advanced undergraduate students. There are no formal prerequisites. It is, however, highly recommended to have basic working knowledge of programming (and strong willingness to learn new environments and languages) since we will need to manipulate data and figures. I will introduce visualization tools that do not require programming. Basic knowledge of statistics will be also helpful. Contact the instructor if you are uncertain about your background.


This website contains all the information in the syllabus. You can simply print this website.


DateTopics Readings, Assignments, and Remarks
Why visualization matters?
1/10Introduction and administrivia Course Material: Further readings:
1/12 Why visualization?,
Statistics 101,
The dark side of visualization
Course material: Further readings:

Assignment #1: Explore Gapminder and identify a relationship that is most fascinating to you. Submit a report explaining the data and the relationship with multiple figures Due date: 1/19, one week

1/17 Visualization examples,
The darkside of visualization (cont'd)
Course material: Further readings: Assignment #2: (i) browse news articles and find an example of bad visualizations we learned. Explain why it is bad and suggest how can we improve it (a sketch will help). (ii) Pick three visualization (bar chart, time series, etc) on mass media and calculate the lie factor (does not need to be super accurate). Due date: 1/26, one week + 2 days
1/19 Data,
Visualization examples,
Data examples
Course material: Further readings:
Elements of Visualization
Logarithmic scale
Course material: Further reading:
Logarithmic scale,
1D data
Course material: Further reading:
1/31 1D data (contd.)
Kernel Density Estimation
Course material: Assignment #3: flip through scientific journals (e.g. Nature, Science, etc) to find one 2D plot (can be scatter plots, line charts, bar charts, etc) in linear scale and create rough sketches of them in log-scale (you can change only one axis to logarithmic scale or both). Then find one plot in log scale and create rough sketches in linear scale. For each case, explain why one scale (log or linear) is better than the other. Due date: 2/7 (one week)
2/2 KDE
Cumulative plots
Course material: Further reading:
2/7 Box plots
Course material: Further reading:
2/9 Guest lecture: Kelly Caine Psychophysics and Basic Human Visual Sensation and Perception
2/14 Log scale
Visual acuity
pixels and aliasing
Course material: Further reading:
2/16 Visual angle
Weber's law
Steven's power law
Course material: Further reading:
2/21 Luminance, Brightness, Colors
Preattentive processing
Course material: Further reading:
Gues lectures
2/23 Guest lecture: Angela Zoss Data Processing and Analysis
2/28 Guest lecture: David Crandall Reconstructing the world from social photo-sharing websites
3/1 Guest lecture: Angela Zoss Comparison techniques
3/6 Project proposal presentation
3/8 Maps Course material: Further reading:
3/13No class (spring recess)
3/15No class (spring recess)
3/20Mercator projection
Course material: Further reading:
Historical visualization
3/22Lily Library Course material:
  • Ptolemy, 2nd cent. [Geographia. Latin] Cosmographia. Ulm : Lienhart Holle, 16 July 1482. G1005 1482 vault
  • Abraham Ortelius. Theatrum orbis terrarum. Originally published: Antwerp: Gillis Coppens van Diest, 1570. G1006 .T37 F8 1570
  • Dutch East India Company Coastal Profiles Boxer II mss vault
  • Breydenbach, Bernhard von. Peregrinationes ad Terram Sanctam. Peter Schoffer, Mainz, 1486. DS 106.B7 vault
  • Louis Bretez. Plan de Paris, Commencé l’Année 1734, et Gravé sous les ordres de Messire Michel Etienne Turgot… (Paris, 1739) G1844 .P3 B844 vault
  • Euclid, Elements (the first english edition, 1570)
  • William Playfair. For the use of the enemies of England: A real statement of the finances and resources of Great Britain; Illustrated by two copper-plate charts. London: Printed by C. Whittingham …, 1796. HG938 .P7
  • Mortality of the British army : at home, at home [sic] and abroad, and during the Russian war, as compared with the mortality of the civil population in England. Published: London : Printed by Harrison and Sons, 1858. DK215 .G7
  • Marey, Dr. E.J. Physiologie médicale de la Circulation du sang basée sur l’étude grahique des mouvements du coeur et du pouls artériel … Paris: Adrien Delahaye, 1863. RC683.5 .C26 M326
  • Serio-comic War Map for the Year 1877 / by F.W. Rose. London: G.W. Bacon, [1877]. Political cartoon map in five colors. 51 x 65 cm. London Low Life DA676 Oversize folder 4, no. 1
  • Marey, E. –J. Physiologie du mouvement. Le Vol des oiseaux. Paris: G. Masson, 1890. QL698 .M32
  • To secure these rights: The report on the President’s committee on Civil Rights. NY: Simon and Schuster, 1947. PS3531 .L7 Box 15 no. 10
Assignment #4: check out oncourse (Due: 4/3)
Graphs and Trees
3/27Progress report 1
3/27 Review: Lily library
Tree map
Course material: Further reading:
3/29 Treemap
Sunburst plot
Bezier curve
Planar graph
Course material: Further reading:
4/3 Bézier curves and splines
Force-directed layouts
Networks and Matrices
Course material: Further reading:
4/5 Hierarchical edge bundling
Force-directed edge bundling
Exploratoy network visualization
convex hull
Course material: Further readling:
4/10Progress report 2
4/10 Network visualization tools
Text visualization
Course material: Further reading:
4/12 Text visualization Course material: Further reading:
4/17 Graph visualization
Course material:
4/19 Course Review Course material:
4/24 Project presentation
4/26Project presentation
4/30Project paper due



By carrying out a visualization project, you will get a hands-on experience of: You can either choose to write an in-depth article about a topic of your choice with data analysis and various visualization techniques or develop a new visualization algorithm or software.


The deliverables are

Project proposal (Due: 3/2)

An one or two page document that contains

Proposal presentation (Due: 3/5, presentation on 3/6)

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: 3/27, #2 Due: 4/10)

Think of it as a draft for the final report, which should contains failures and successes in the course of your project. It should address: what you have tried, what didn't work, what did work, and what you have learned from it. There is no specific format. Try to document details that you have done. For instance, if you try to learn D3.js, summarize the things that you learned, show screenshots or codes of the visualizations that you played with, in learning it. Document your design sketches, brainstorming ideas, failed plots, etc.

Final report (4/28)

Prepare two documents: one is a polished document that resembles an academic paper with the following elements. In the other document, document your *sweat* on the project. Record your failures and successes and desribe what you have learned about visualization through your experiences.

Final presentation (4/24, 4/26)

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.



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



Softwares, libraries, and data


Network visualization



See YYiki: Information visualization books