DATA VISUALIZATION AND MINING

Graduate

This course introduces students to the statistical methods involved in visualization of high dimensional data, including interactive methods directed at exploration and assessment of structure and dependencies in data. Topics include methods for finding groups in data including cluster analysis, dimension reduction methods including multi-dimensional scaling, pattern recognition, and smoothing techniques. Graduate students are required to complete an additional project and report. This will be defined in consultation with the instructor to ensure the scope and assessment are sufficiently advanced to warrant graduate level credit. This course is cross-level listed with STAT 4660. Credit cannot be received for both MCS 8660 and STAT 4660. PREREQUISITE: Admission to a graduate program in Science HOURS OF CREDIT: 3