3 Cr. (Hrs.:3 Lec.)
Provides a grounding in data mining techniques and prepares students to design, use, and evaluate these techniques in a variety of application domains and for the purpose of decision support. Topics include decision trees, rule based systems, statistical approaches, neural networks, and instance based approaches. Prerequisites: (CSCI 110, CSCI 117, or CSCI 135), M 121 or higher, (CAPP 158 or CSCI 340). (1st)
Course generally offered fall (1st) semester.
E1. Students have basic computer skills and familiarization with common microcomputer applications, including web browsing, email, text editing, spreadsheets, and file manipulation.
E2. Students have had College Algebra (M121) or the equivalent.
R1. Students can identify key characteristics of data mining and/or decision support projects, and can use these characteristics to choose appropriate data mining techniques.
R2. Students understand and can apply data preprocessing techniques appropriately.
R3. Students understand the underlying theory, biases, strengths, and weaknesses of different data mining techniques.
R4. Students understand and are able to apply measures of success to algorithm output, and can measure performance differences between algorithms.
R5. Students are able to use data mining algorithms including decision trees, rule based systems, statistical approaches, instance based approaches, linear techniques, and clustering, to both example data sets and real life data sets.
R6. Students have a firm grasp of supervised and unsupervised approaches to data mining and when to use each type.
R7. Students have a high-level understanding of additional data mining techniques including neural networks, genetic algorithms, and fuzzy logic.