CSC 423 - Data Mining

Units: 3
When Offered: Fall
The course commences with an examination of the knowledge discovery process. In particular, the introduction equips students with the strategic thinking skills essential to focus on cutting edge data mining techniques that can be applied in a wide variety of settings, e.g., business, engineering, health care, science, etc. Traditional topics include data mining algorithms and implementation issues, advantages and disadvantages of data mining, and examples of knowledge engineering. Current topics such as ubiquitous, distributed, and spatiotemporal geographic data mining will also be explored. This is a practical hands-on course that culminates in a real-world project implemented via open source tools.
Grade Type: Letter Grade
Prerequisite(s): EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course