CSC 591V: Visual and Data Analytics
MWF 11:45-12:35 1220 EB-II
| Instructor: | Christopher G. Healey | |
| Contact: | 2266 EB-II healey@csc.ncsu.edu |
|
| Office Hours: | 9:30-10:30 M in 2266 EB-II, or by appointment |
My goals for you are to:
The focus during this class will be on the visual aspects of data and visual analytics. However, time permitting, we will discuss in some detail data management and analysis techniques like dimensional reduction, rule mining, segmentation, clustering, and segmentation.
There are no assigned textbooks for this course. Topics will be covered during in-class lectures, and through course notes made available on this web page.
Links to the supplementary material in the form of research papers related to each topic are included in this syllabus. PDF for most papers is available through the NCSU library web site, which has full-text access to most recent ACM and IEEE journals and conferences. A number of supplemental graphics and visualization textbooks are also available:
This course offers an introduction to data and visual analyatics, to data visualization, and to research techniques from computer graphics and human perception that form the foundations for numerous visualization algorithms. We will cover the basic visual analytics pipeline, which converts raw data into images that viewers can use to study datasets in efficient and effective ways. Common methods to analyze large, complex datasets will be discussed, including dimensional reduction, clustering, rule mining, segmentation, and summarization. Systems for scientific and information visualization will be presented, along with basic guidelines from computer graphics and human perception that relate to these techniques. Assignments will employ a "visualization by design" methodology, asking students to create prototype visualizations by hand, then implement their ideas for real data from either an astrophysics or a movie recommender domain.
Below is a tentative course schedule. Please note that time frames and topics will be confirmed in class and are subject to possible changes.
Apart from material and on-line versions of papers presented during class, no additional readings will be assigned.
Four homework assignments will be assigned as part of this course. The first two assignments involve designing and presenting proposed solutions for two data sources: (1) a scientific dataset containing two-dimensional flow data (flow vectors) on a regular grid, taken from a simulated supernova collapse, and (2) an information dataset containing recommendations and properties for a collection of movie recommendations, generated by the MovieLens recommender system.
Following the in-class presentations of each student’s proposed design, you will be asked to pick one design, the implement (Assignment 3) and test and validate (Assignment 4) your system. You can implement one of your proposed designs, or a recommendation from one of the other students. We will provide visualizations from existing system that you can use to assess your own system’s performance and abilities. You will be expected to demonstrate your completed system, and enumerate its strengths and limitations, in-class.
Each assignment counts for 15% of your final grade. A final exam will count for the remaining 40%. There is no midterm exam during this course. Final grades will be calculated using +/- grading:
Missed assignments cannot be made up without an official university excuse. Contact me as soon as possible if you need to discuss reasons for late or missed assignments.
If you miss (or plan to miss) class(es), contact me as soon as possible to identify the material to be covered during your absence. You are expected to "make up" the material by reading on-line material or obtaining summaries of the lectures from your classmates.
Graduate student status in the Department of Computer Science, or by permission of the instructor. All students are expected to have (or obtain, on their own) expertise in at least one high-level language and graphics API (e.g., C/C++ and OpenGL, Java plus Java2D or Java3D), plus experience designing and implementing a medium-sized software project.
The university provides a detailed policy on academic integrity. This policy can be found in the Code of Student Conduct. It is understood that when you submit your homework, you are implicitly agreeing to the university honor pledge: "I have neither given nor received unauthorized aid on this test or assignment."
Academic dishonesty (e.g., cheating or plagiarism) will not be tolerated under any circumstances. If you are having difficultly with any part of the course material, please see me as soon as possible. I will do everything I can to help you with any course-related problems you may be having. If you are found to be guilty of academic dishonesty, however, I will then do everything I can to see that you are punished as forcefully as possible. This may include asking to have you suspended or expelled from the course, the program, and/or the university. At a minimum, you will receive -50% for the assignment in question, and your name will be placed on record with the university as having committed an academic offence. Multiple offences during your academic career will result in suspension or expulsion from the university. I take absolutely no pleasure in pursuing cases of academic misconduct, and would ask that you please do not put me in this position.
All effort will be made to ensure that no students with disabilities are denied any opportunity to successfully complete this course. If you have specific requirements that need to be addressed, please contact me immediately. Possible changes can include (but are not necessarily limited to) rescheduling classes from inaccessible to accessible buildings, or providing access to auxiliary aids such as tape recorders, special lab equipment, or other services such as readers, note takers, or interpreters. This may also include oral or taped tests, readers, scribes, separate testing rooms, or extension of time limits.
None.
None.