Data Mining

Catalog description

This course introduces the theories and tools for intensive data analysis methods and data-mining techniques such as rule-based learning, decision trees, clustering, and association rule mining. This course also covers interpretation of the mined patterns using visualization techniques and offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Data analysis software is introduced via currently used tools in the industry. In addition to expanding upon the earlier introduced approaches to discerning and validating patterns in data through sound applications of the scientific method, with a particular emphasis on hypothesis testing, the notion of statistical significance, and tests of difference, the goal of this course is to endow students with the fundamental data management, review, re-engineering, and exploration skills, as necessary data analytical competencies. Discussions include the main data-mining methods currently used, including data preparation, cleaning, testing, training, clustering, classification, association rule mining, decision networks, and other common data-mining algorithms and techniques.