Classical and advanced methods of visual data analysis within scientific applications context; emphasis on examples of scientific investigation with visual tools, and new visual methods with computer graphics; visual data analysis of numerical experimental and analytical results including: gradients, function-extraction, chaos, nth-order tensor glyph representations, molecular synthesis.
(3H, 3C), II.
Pre: Math 1015-1016 or Math 1205-1206, any course in C, Fortran, or Pascal, working knowledge of Unix.
Co: Senior or graduate project that demonstrates a need for visual data analysis.
SCI VISUAL DATA ANALYSIS
Upon successful completion of this course, the student will be able to:
The need for visual data analysis has penetrated almost every discipline that encounters these large data sets. Because large data sets are encountered in the course of a senior project or graduate level research project, this course is oriented as a problem solving class project. Students are required to define a research problem as their class project that can benefit from implementing visual methods and techniques introduced in the class. Because of the emphasis on research, this course is suited for both 4000 level undergraduate class and 5000 level graduate class where senior projects, M.S. and Ph.D. research projects can encounter large complex data sets. As a special study this class was taken mostly by graduates and the Department of Computer Science also indenpently approved this class for graduate credit.
No credit is given for developing or programming visual interfaces although it is expected that students must demonstrate proficiency in programming as a prerequisite.
With resources provided in the ACITC: 1) Scientific Visualization and Modeling Classroom and 2) University Visualization and Animation Laboratory, students learn how to use state-of-the-art visual tools in a systematic and rational way, independent of the source of data (experimental, numerical, or analytical) to gain insight into their data or complex analytic functions. Access to NSF supercomputer time is also available from the NCSA for class projects that require numerical solutions to complex boundary value problems.
Corequistes: Existing project (e.g. Senior Design Project, Masters Thesis or Ph.D. dissertation) that will benefit from their class project.
Percent of Course 1. Historical perspective on visual tools and methods in scientific research: 5 % general principles for using graphical methods for visual insight 2. Scientific visual data analysis 15 % A. General principles and methods 1. Data compression into 4-D space: gradients of scalar functions 2. N-dimensional space for extracting new relationships 3. Visual representation of N-th order tensors B. Introduction to visual tools 25 % 1. Constructing scientific data sets and data types and conversions 2. General visual tools 3. Interactive data languages and visual programming systems C. Examples 5 % 1. Fluid mechanics, solid mechanics and dynamics 3. Multimedia development A. Foundations of multimedia 13 % 1. Introduction to multimedia 2. Introduction to layout and design B. Authoring tools 12 % 1. Macromedia director 2. Macromedia authorware professional C. Technical Labs 25 % 1. Tour of the multimedia lab 2. Interface design and image scanning 3. Digital audio, digital video, navigation and scripting 4. Final project production 4. Final Class Presentations
Revised September 27, 2007