ECE 695D - Introduction to Visual Analytics
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.
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:
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:
There is no final exam in this course. There is, however, a take-home midterm exam that will be administered halfway through the course.
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.
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 (firstname.lastname@example.org) or office phone (765-494-0364).
Please see the detailed page on CampusEmergencies for more information.
See the following links for older versions of this course: