This course will provide an introductory look at concepts and techniques in the field of data mining. After covering the introduction and terminologies to Data Mining, the techniques used to explore the large quantities of data for the discovery of meaningful rules and knowledge such as market basket analysis, nearest neighbor, decision trees, neural networks, and clustering are covered. The students learn the material by implementing different techniques throughout the semester (3-0-3).

This course provides an overview to database management systems such as hierarchical, network, object oriented and relational. The course covers fundamental database concepts with the focus on the relational DBMS. Students in detail learn relational data modeling and design, abstract relational languages, and structured query language (SQL). The DBMS implementation topics including file organization, indexing, and query processing are discussed. An overview of query optimization, recovery, and concurrency control is given in the course. Students build a database application for their course project.(3-0-3) (T) (C)

Prerequisite: CS331 or CS401 and Strong Programming knowledge.
Overview of fundamental issues of information retrieval with theoretical foundations. The Information-retrieval techniques and theory, covering both effectiveness and run-time performance of information-retrieval systems are covered. The focus is on algorithms and heuristics used to find documents relevant to the user request and to find them fast. The course covers the architecture and components of the search engine such as parser, stemmer, index builder, and query processor. The students learn the material by building a prototype of such a search engine. (3-0-3)