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  • Saturday, November 30: I have created a Blackboard site for the course for distributing the grades. To access, please go to and see ECE-69500-166. Your current grades can be found under the page titled "My Grades"; the only missing grades so far is for the analytical exercise.
  • Monday, November 25: The deadline for the final paper has been moved to Tuesday, November 26. Make sure that I have everything by Wednesday morning so that I can send out the review assignments. If you have already submitted, you are welcome to submit a new version if desired.
  • Monday, November 18: Prof. Irani's guest lecture on November 26 has been canceled. This means that we can move our first project presentation to day that instead of November 21. See the detailed schedule.
  • Tuesday, November 12: It is time to schedule beta release demonstrations! Available times are Friday 9am-11.30am and 2pm-4pm. Sign up by filling in a time in the "15-min Demonstration" column for your project in the spreadsheet entitled "Project Groups".
  • Thursday, November 7: Please remember that midterms are due to be submitted tonight (as long as I have it by tomorrow morning)! Please submit by e-mail to Prof. Elmqvist.
  • Wednesday, October 10: Tomorrow we will continue with discussing data representations and transformations. Please note that there are no paper summaries due this week or the next!
  • Tuesday, September 24: I have postponed the deadline for Design documents one week to October 6!
  • Sunday, September 8: Please remember that CourseProject proposals are due by midnight tonight! As long as I have it by tomorrow morning, I am happy. Submit by email.
  • Sunday, September 1: Bear with me as I add references to this week's paper summary chapter (Chapter 3).
  • Wednesday, August 28: Don't forget the first deadline for the first phase of the AnalyticalExercise that is due tomorrow at midnight (Thursday, August 29)!
  • Sunday, August 18: The first lecture will be given on Tuesday, August 20. Meanwhile, this website will slowly be updated with information. See you on Tuesday!

ECE 695D - Introduction to Visual Analytics

  • Course hours: TTh 9:00am-10:15am in HAMP 2117
  • Term: Fall 2013
  • Instructors:
    • Niklas Elmqvist, Assistant Professor of Electrical and Computer Engineering
      • E-mail:
      • Office: MSEE 270 (second floor of the MSEE building)
      • Office hours: Thursdays, 10:30am-11:30am (or by appointment)
    • David Ebert, Silicon Valley Professor of Electrical and Computer Engineering
      • E-mail:
      • Office: POTR 228
      • Office hours: by appointment only
  • Textbook: No official textbook, research papers (see the ReadingList).
  • Prerequisites: Introductory knowledge of one or more of the following areas: data analysis, knowledge management, statistics, computer graphics, visualization. (No formal prerequisites.)
  • Course Schedule: See the DetailedSchedule.
  • Assignments:

Note: This website serves as the formal syllabus for ECE 695D. It is subject to change as the course progresses, so please keep coming back to find more information!


The development of terascale and petascale computing systems and powerful new scientific instruments has created an unprecedented growth of scientific data. Unfortunately, this sea of data has not had the transformational effect that was expected on science and engineering, but has instead become a bottleneck in itself. Fundamental new techniques are needed to turn this data deluge into the useful, understandable information and knowledge necessary for transformational science and engineering, creating interactive knowledge environments for exploration, design, and discovery.

Visual analytics---the science of combining interactive visual interfaces with automatic algorithms to support analytical reasoning and build synergies between humans and computers---is an emerging science that has been created to address this challenge. Originally launched in 2004 by the U.S. Department of Homeland Security to enable harnessing the data deluge in tackling the overarching economical, environmental, and security-related challenges facing society, the discipline now has funded efforts from the National Science Foundation and the U.S. Department of Energy, as well as several international efforts of similar scale and magnitude.

Visual analytics is used in all areas spanning science, engineering, business and government; examples of specific application areas include intelligence analysis for homeland security, business intelligence support to help businesses acquire a better understanding of their commercial context, mobile graphics for emergency first responders, network log analysis for computer security, and health monitoring for disease outbreak prediction and response.

This course will serve as an introduction to the science and technology of visual analytics. The course contents will include both theoretical foundations of this interdisciplinary science as well as practical applications of integrated visual analysis techniques on real-world problems.


This course will cover topics in visual analytics. The format for the course will be group discussions of papers, lectures by the instructors, and student presentations of papers. There will also be a class project, paper summaries in an annotated bibliography, and a take-home midterm exam. The grading will be based on participation in class, critical assignments, and class projects. Class projects may be done individually or in groups. Projects have the potential of leading to work that forms the basis of an undergraduate research projects, Master's thesis, or Ph.D. research topic.


See the DetailedSchedule for more information.


There is no official textbook for this course. Students will read and discuss seminal and current technical research papers. A list of readings (in progress and subject to frequent update) is available on the ReadingList.

The following books may be useful as references.

  • Illuminating the Path: The R&D Agenda for Visual Analytics, Editors: James J. Thomas and Kristin A. Cook (online version)
  • The Visualization Handbook, by Charles Hansen and Christopher Johnson, Academic Press, 2005
  • The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, by William Schroeder, Ken Martin, Bill Lorensen, 2nd Edition, 1997, (ISBN 0-13-954694-4).
  • Readings in Information Visualization: Using Vision to Think, by Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman, Morgan Kaufmann

Assessment Methods

The course outcomes will be assessed through student demonstration of a completed visual analytics project, submission of working program(s), oral and written presentation of results (literature survey, alpha release report, beta release report, regular meetings of project teams with the instructor, and the final project report), and a take-home midterm exam. The overall knowledge acquisition of visual analytics will be assessed by student oral presentations of papers, through the completion of a literature review, and through several initial project assignments.


Grades will be assigned on the following grounds:

  • Paper summaries (reading and evaluation) - 20%
  • Analytical exercise - 10%
  • Paper presentations and class participation - 10%
  • Midterm exam - 10%
  • Class project - 50%

Submissions may be turned in up to one week after the due date with a 30% grade penalty. Phases will not be accepted more than a week late. If you have a research deadline or other permissible excuse, please speak with the instructor ASAP to resolve late and missed submissions.

Plus/minus grading will be used in this class.


This course has five types of assignments: an introductory visualization assignment, weekly paper critiques, paper presentations, and the course project. More details on these are given below:

  • Analytical Exercise: To get a firsthand experience of some of the problems that our potential end users face, you will perform an analytical reasoning exercise to find a hidden threat in collection of police and intelligence reports. The exercise will not involve any programming and can be done entirely by hand on paper.
    • Released: Tuesday, Aug 20 (Week 1)
    • Deadline (Phase 1): Thursday, Aug 29 (Week 2)
    • Deadline (Phase 2): Thursday, Sep 5 (Week 3)
  • Paper Summaries: Each student taking this course for credit must write a summary of a research paper every week (see the PaperSummaries document).
    • Deadline: every Sunday at midnight (with some exceptions)
  • Paper Presentations: Students must present two (2) research papers in-class during the course. Presentations should be 10-15 minutes.
  • Course Project:
    • Released: Tuesday, Aug 20 (Week 1)
    • Deadlines: several milestones
      • Thursday, Sep 5 (Week 3) - Project Proposal
      • Sunday, Sep 22 (Week 5) - Literature Review
      • Sunday, Sep 29 (Week 6) - Design
      • Sunday, Oct 20 (Week 8) - Alpha Release
      • Sunday, Nov 10 (Week 12) - Beta Release
      • Sunday, Nov 17 (Week 13) - Paper Draft
      • Sunday, Nov 24 (Week 14) - Final Paper
      • Last week of classes (Week 16) - Presentations
      • Sunday, Dec 8 (Week 16) - Final Release
      • Sunday, Dec 8 (Week 16) - Final Reviews

There is no final exam in this course. There is, however, a take-home midterm exam that will be administered halfway through the course.

Academic Integrity

Students must conform to Purdue's policy on academic integrity. More specifically, Purdue prohibits "dishonesty in connection with any University activity. Cheating, plagiarism, or knowingly furnishing false information to the University are examples of dishonesty." [University Regulations, Part 5, Section III, B, 2, a]

Please see the detailed page on AcademicIntegrity. Note that you are responsible for knowing this information---ignorance is not a valid excuse.

Campus Emergencies

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. Information about changes in this course can be received from the course website, or the Blackboard site, or by contacting the instructor by email ( or office phone (765-494-0364).

Please see the detailed page on CampusEmergencies for more information.

Previous Years

See the following links for older versions of this course:

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Page last modified on November 30, 2013, at 10:33 AM