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Course :
STAT 745 – Data Mining
Semester : Fall 2008
Course format and credit hours :3 hr Lecture
Prerequisites : STAT 545, or equiv
Instructor : Gerry Hobbs, 3-1093 , [email protected]
Course Time & Location : M/W 1:00-2:15, H432
Office Hours: M (3:30-4, HSC 3308), W (2:15-4:30, H408) & by appt.
Course Objectives : The objectives of this course are to acquaint students with the philosophy
and methods of data mining. In addition to learning the general approach to the subject we will
focus on data preparation, assessment and modeling methods.
Expected Learning Outcomes : Upon successful completion of this course:
Students will understand ways in which the methods of data mining differ from the more
classical statistical approaches to data analysis.
Students will understand the rationale for data mining methods and why those methods
are inappropriate in classical settings.
Students will be conversant with the transformations commonly applied in data mining
Students will understand the notion of imputation and a wide variety of imputation
methods that are used in marketing, medical, genomic and financial applications.
Students will have an understanding of generalized linear regression methods including
logistic regression with a mixed set of predictors.
Students will have an understanding of neural network methods and applications.
Students will understand decision tree models and methods including CART, CHAID and
C4.5 with application to both regression trees and classification trees
Students will understand both the graphical and analytical assessment tools that are
popular in the field.
Students will learn how to use a major software program that is widely used in American
industry for data mining applications.
Required Text: Principles of Data Mining by Hand, Mannila and Smyth
Grading :
numerous quizzes
80 %
20 %
100 %
Grade Assignment : Based on point earned on the quizzes and the project. The students with
the most points will get the best grades. Quizzes will be given about once a week.
Grading Policy :
There are no make-up quizzes except by prior arrangement with instructor
Exam grading appeals in writing on the day the exam is returned.
Near the end of the course you will be assigned a problem. Groups of
from 2 to 4 students will work on the same problem together. The project
will be due at the final exam period.
Attendance Policy:
Social Justice
Statement :
Consistent with WVU guidelines, students absent from regularly
scheduled quizzes because of authorized University activities will have the
opportunity to take them at an alternate time.
Make-up exams for
absences due to any other reason will given be at the discretion of the
“West Virginia is committed to social justice. I concur with that
commitment and expect to maintain a positive learning environment based
upon open communication, mutual respect, and nondiscrimination. Our
University does not discriminate on the basis of race, sex, age, disability,
veteran status, religion, sexual orientation, color or national origin.
If you are a person with some sort of disability and need accommodation
in order to participate in this class please advise me of the specifics and
make appropriate arrangements with Disability Services (293-6700).”