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IE 59000: Data Visualization: Theory and Practice
Instructor
Ji Soo Yi yij@purdue.edu, Wang 4510
Teaching
Assistant
TBA
Lectures
10:30 – 11:20am (EST/EDT), M/W/F, Wang 2555
Textbook
Murray, S. (2013). Interactive Data Visualization for the Web. O’Reilly Media,
Inc. ISBN: 978-1-4493-3973-9 (Purdue library online access)
Optional
Textbook
Yau, N. (2011). Visualize this: The FlowingData guide to design, visualization,
and statistics. Wiley. ISBN: 0470944889 (Purdue library online access)
Websites
Blackboard Learn (https://mycourses.purdue.edu/) will be mainly used to distribute materials for the class and submissions of the homework and examinations,
and Piazza (https://piazza.com/purdue/spring2015/ie59000) will used for Q&A.
COURSE DESCRIPTIONS
This course will cover the basic principles of data visualization through 1) understanding data and
potential tasks; 2) designing a proper visualization tool; 3) implementing the visualization tool; 4)
generating insights from resulting data visualizations; and 5) disseminating the results to the general public via web-based visualization. Students are expected to learn the basic principles and
theories of data visualization as well as how to implement visualizations using D3.js
(http://d3js.org). In other words, the course is designed to strike a balance between theory and
practice as the course name implies.
There is no prerequisite for this course, but it will be helpful to know basic data handling using
various script languages (e.g., Ruby and Python), statistical analysis (e.g., R, SPSS, and SAS), web
programming (e.g., HTML, CSS, JavaScript, jQuery, Ruby on Rails), and database handling (e.g.,
MySQL). The necessary computer programming skills will be discussed briefly in the class, and
relevant online tutorials (e.g., http://www.theodinproject.com/) will be shared. However, the students of this course will be expected to learn the necessary skills by themselves as necessary, so
that they can construct interactive, web-based visualizations.
Note: The syllabus is not complete. Minor details could be changed before the semester starts.
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GRADING
Items
5 HWs
Exam 1
Exam 2
P1
P2
Total
Extra Credits
Percentages
10 % (2% for each)
20 %
20 %
10 %
20 %
100 %
Up to 3 %
The final grade will NOT be curved, and the distribution of grades will adhere to the following
table.
Ranges
[0.00, 60.00)
[60.00, 63.33)
[70.00, 73.33)
[80.00, 83.33)
[90.00, 93.33)
Grades
F
DCBA-
Ranges
Grades
Ranges
Grades
[63.33, 66.66)
[73.33, 76.66)
[83.33, 86.66)
[93.33, 96.66)
D
C
B
A
[66.66, 70.00)
[76.66, 80.00)
[86.66, 90.00)
[96.66, +∞)
D+
C+
B+
A+
CLASS POLICY
Attendance

Attendance is not required for this class.
Homework

Late submissions will not be graded without any exception.
Examinations





Examinations 1 and 2 will be 3-hour-long, take-home, open-book, open-discussion examinations (9pm to 11:59pm) on each examination day. Each student should make him or her
available during the time. If a student is not available for the examination duration due to
unavoidable reasons, the student should contact the instructor separately to schedule a separate exam in various forms based on students’ availability.
Examinations are accumulative. In other words, Examination 2 covers the materials covered by Examination 1.
Students will have a dry run prior to Examination 1 in order to help each student double
check all the logistics (e.g., internet connection and Blackboard submission links) prior the
examinations. If a student has any individual difficulties in properly taking an examination,
the student is responsible for resolving these issues. If there is an issue for all the students
at the same time, the instructor will make an announcement to handle the issue.
Late submissions will not be graded without any exception.
Since the exams are open-discussion, students can discuss the examination questions with
each other, but they should write the answer on their own. When any sign of plagiarism is
identified, it will be reported to Office of the Dean of Students.
Note: The syllabus is not complete. Minor details could be changed before the semester starts.
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Re-grading

Requests for re-grading project outcomes and reading assignments will be considered only
with a written explanation using the template (which will be shared through Blackboard
Learn), submitted after at least 24 hours of self-evaluation, and only within ONE-WEEK
from the time the graded work is returned.
Misconduct


Any types of Misconduct defined in Student Conduct of the University Regulations will
not be tolerated.
Submitted examination responses and homework will be reviewed through plagiarism detection systems (e.g., SafeAssign and Moss1).
EMERGENCY SITUATIONS

In the event of a major campus emergency, course requirements, deadlines and grading
percentages are subject to changes that may be necessitated by a revised semester calendar
or other circumstances beyond the instructor’s control. Here are ways to get information
about changes in this course.
Instructor’s email address: yij@purdue.edu
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http://theory.stanford.edu/~aiken/moss/
Note: The syllabus is not complete. Minor details could be changed before the semester starts.
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LECTURE SCHEDULE2
Date
M 1/12
W 1/14
F 1/16
M 1/19
W 1/21
F 1/23
M 1/26
W 1/28
F 1/30
M 2/2
W 2/4
F 2/6
M 2/9
W 2/11
F 2/13
M 2/16
W 2/18
F 2/20
M 2/23
W 2/25
F 2/27
M 3/2
W 3/4
F 3/6
M 3/9
W 3/11
F 3/13
M 3/16
W 3/18
F 3/20
M 3/23
W 3/25
F 3/27
M 3/30
W 4/1
F 4/3
M 4/6
W 4/8
F 4/10
M 4/13
W 4/15
F 4/17
M 4/20.
W 4/22.
F 4/24.
M 4/27
W 4/29
F 5/1
2
Topic
D1: Introduction to IE 59000
L1: Introduction
D2: Overview
Martin Luther King Jr. Day
L2: Introducing D3
D3: Visual Perception
D4: Values/Benefits of visualization
L3: Technology Fundamentals
D5: Multivariate data & table/graph design
D6: Few’s design guidance
L4: Setup, L5: Data
D7: Multivariate visualizations 1
D8: Multivariate visualizations 2
L6: Drawing with Data
D9: Tasks and analysis
D10: InfoVis systems and toolkits
L7: Scales
D11: Commercial systems
D12: Storytelling
L8: Axes
D13: Tufte’s design principles
D14: Casual visualizations
L9: Updates, Transitions, and Motion
D15: Graphs and networks 1
D16: Graphs and networks 2
No class
D17: Hierarchies and trees 1
Spring Vacation
Spring Vacation
Spring Vacation
D18: Hierarchies and trees 2
L10: Interactivity
D19: Interaction
D20: Overview & details
L11: Layouts
D21: Text & documents 1
D22: Text & documents 2
L12: Geomapping
D23: Visual analytics 1
D24: Visual analytics 2
L13: Exporting
D25: Time series data
D26: Evaluation
Group Project Time
No class
P2 Presentation
P2 Presentation
P2 Presentation
Reading
Comments
Ch1
Ch2
HW1 (10pm, Sun 1/25)
Ch3
Ch4, 5
HW2 (10pm, Sun 2/8)
Ch6
Ch7
HW3 (10pm, Sun 2/22)
Ch8
Ch9
-
Exam 1, 9-11:59pm
Ch10
HW4 (10pm, Sun 3/29)
Ch11
Ch12
HW5 (10pm, Sun 4/12)
Ch13
Exam 2, 9-11:59pm
The schedule could be changed as the class evolves.
Note: The syllabus is not complete. Minor details could be changed before the semester starts.
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READING LIST
The following reading list contains the relevant literature for each lecture (e.g., R2 is corresponding to
L2). Most of the literature is available through Purdue Library, and unavailable ones will be distributed
through Blackboard Learn.
R2: C. North, "Information Visualization", in Handbook of Human Factors and Ergonomics, G. Salvendy (editor), John Wiley & Sons, 2005.
R3: C. Healey, "Perception in Visualization," NC State, http://www.csc.ncsu.edu/faculty/healey/PP/index.html
R4: J.-D. Fekete, J. van Wijk, J. Stasko, C. North, "The Value of Information Visualization", in Information Visualization: Human-Centered Issues and Perspectives, (Editors: A. Kerren, J. Stasko, J.D. Fekete, C. North), Springer, 2008, pp. 1-18.
R5: S. Few, "Effectively Communicating Numbers - Selecting the Best Means and Manner of Display",
2006. http://www.perceptualedge.com/articles/Whitepapers/Communicating_Numbers.pdf
R6: Now You See It, chapters 5-12
R7: J. S. Yi, R. Melton, J. Stasko, and J. Jacko, "Dust & Magnet: Multivariate Information Visualization
using a Magnet Metaphor," Information Visualization, Vol. 4, No. 4, Winter 2005, pp. 239-256.
R8: R. Spence and L. Tweedie, "The Attribute Explorer: information synthesis via exploration", Interacting with Computers, Vol. 11, pp. 137-146, 1998.
R9: M. Sedlmair, M. Meyer, T. Munzner, "Design Study Methodology: Reflections from the Trenches
and Stacks", IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, Dec.
2012, pp. 2431-2440.
R10: M. Bostock, V. Ogievetsky, J. Heer, "D3: Data-Driven Documents", IEEE Trans. on Visualization
and Computer Graphics, Vol. 17, No. 12, Dec. 2011, pp. 2301-2309.
R11: M. Spenke and C. Beilken, "InfoZoom - Analysing Formula One racing results with an interactive
data mining and visualization tool," Proceedings of 2nd Intl. Conf. on Data Mining, July 2000.
R12: E. Segel and J. Heer, "Narrative Visualization: Telling Stories with Data", IEEE Trans. on Visualization and Computer Graphics, Vol. 16, No. 6, Nov.-Dec. 2010, pp. 1139-1148.
R13: S. Bateman, et al, "Useful Junk? The Effects of Visual Embellishment on Comprehension and
Memorability of Charts", Proceedings of CHI '10, April 2010, pp. 2573-2582.
R14: Z. Pousman, J. T. Stasko and M. Mateas, "Casual Information Visualization: Depictions of Data in
Everyday Life", IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 6, November/December 2007, pp. 1145-1152.
R15: B. Lee, C. Plaisant, C. Sims Parr, J.-D. Fekete, N. Henry, "Task Taxonomy for Graph Visualization", Proc. of BELIV '06, April '06, pp. 1-5.
R16: A. Perer, B. Shneiderman, "Balancing Systematic and Flexible Exploration of Social Networks,"
IEEE Trans. on Visualization and Computer Graphics, Vol. 12, No. 5, Sep.-Oct. 2006, pp. 693700.
R17: S. Card and D. Nation, "Degree-of-Interest Trees: A Component of an Attention-Reactive User Interface", Proc. of AVI '02, May 2002, pp. 231-245.
R18: B. Johnson and B. Shneiderman, "Tree-maps: A Space Filling Approach to the Visualization of Hierarchical Information Structures", Proc. of Vis '91, Oct. 1991, pp. 284-291.
R19: J.S. Yi, Y.A. Kang, J.T. Stasko and J.A. Jacko, "Toward a Deeper Understanding of the Role of
Interaction in Information Visualization", IEEE Transactions on Visualization and Computer
Graphics, Vol. 13, No. 6, Nov/Dec 2007, pp. 1224-1231.
Note: The syllabus is not complete. Minor details could be changed before the semester starts.
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R20: B. Bederson et al, "DateLens: A fisheye calendar interface for PDAs," ACM Trans. on CHI, Vol.
11, No. 1, March 2004, pp. 90-119.
R21: F. Viegas, M. Wattenberg, "Tag Clouds and the Case for Vernacular Visualization", interactions,
Vol. 15, No. 4, Jul-Aug 2008, pp. 49-52.
R22: M. Wattenberg, F. Viegas, "The Word Tree, an Interactive Visual Concordance", IEEE Trans. on
Visualization and Computer Graphics, Vol. 14, No. 6, Nov.-Dec. 2008, pp. 1245-1252.
R23: D. Keim, G. Andrienko, J.-D. Fekete, C. Gorg, J. Kohlhammer, and G. Melancon, "Visual Analytics: Definition, Process, and Challenges", in Information Visualization: Human-Centered Issues
and Perspectives, (Editors: A. Kerren, J. Stasko, J.-D. Fekete, C. North), Springer, 2008, pp. 1-18.
R24: J. Stasko, C. Gorg, and Z. Liu, "Jigsaw: Supporting Investigative Analysis through Interactive Visualization", Information Visualization, Vol. 7, No. 2, Summer 2008, pp. 118-132.
R25: W. Aigner, S. Miksch, W. Muller, H. Schumann, C. Tominski, "Visual Methods for Analyzing
Time-Oriented Data", IEEE Trans. on Visualization and Computer Graphics, Vol. 14, No. 1, Jan.Feb. 2008, pp. 47-60.
R26: S. Carpendale, "Evaluating Information Visualizations", in Information Visualization: HumanCentered Issues and Perspectives, (Editors: A. Kerren, J. Stasko, J.-D. Fekete, C. North), Springer,
2008, pp. 19-45.
Note: The syllabus is not complete. Minor details could be changed before the semester starts.
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