ST562 - Data Mining with SAS Enterprise Miner
- Prerequisites: ST 512 or ST 514 or ST 515 or ST 517
- Term & Frequency: Spring
- Student Audience: Graduate Students in all fields
- Credit: 3 credits
- Recent Texts: Applied Analytics Using SAS Enterprise Miner, pdf file available for registered course participants
- Recent Instructors: David Dickey
- Background and Goals: The goal of this course is to introduce the basic elements of data mining techniques to students with backgrounds equivalent to that supplied by the department's statistical methodology sequence. Students will get hands-on experience with the SAS Enterprise Miner product as well as SAS programming through in class demonstrations and practice with homework data sets.
- Content: This is a hands-on course using modeling techniques designed mostly for large observational studies. Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis. Clustering and association analysis are covered under the topic "unsupervised learning," and the use of training and validation data sets is emphasized. Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves. SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful.
- Alternatives: ST 563 Online
- Subsequent Courses: ST 564 Online
S1 2017 Sections:
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