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Graduate Course Proposal Form Submission Detail - ISM6137
Tracking Number - 2626

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Current Status: Approved by SCNS - 2012-04-09
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments: to GC; pndng cat/concur- CSE, Math. for Finance Prog Changes (apprd 11/14/11); to GC 11/28/11. Rev. Obj . Emld fac 1/13/12. OKd. To GC 2/6/12; 2/20/12. Appd. To USF Sys 2/20/12. to SCNS 2/28/12. Appd eff 4/15/12. Banner. Subm 6830. Appd as 6137

Detail Information

  1. Date & Time Submitted: 2011-09-19
  2. Department: Information Systems and Decision Sciences
  3. College: BA
  4. Budget Account Number: 1407000
  5. Contact Person: Kaushal Chari
  6. Phone: 8139746768
  7. Email: kchari@usf.edu
  8. Prefix: ISM
  9. Number: 6137
  10. Full Title: Statistical Data Mining
  11. Credit Hours: 3
  12. Section Type: C - Class Lecture (Primarily)
  13. Is the course title variable?: N
  14. Is a permit required for registration?: N
  15. Are the credit hours variable?: N
  16. Is this course repeatable?:
  17. If repeatable, how many times?: 0
  18. Abbreviated Title (30 characters maximum): Stat Data Mining
  19. Course Online?: C - Face-to-face (0% online)
  20. Percentage Online: 0
  21. Grading Option: R - Regular
  22. Prerequisites: one course in basic statistics or equivalent
  23. Corequisites: none
  24. Course Description: Development of statistical concepts and methods for mining large business data bases

  25. Please briefly explain why it is necessary and/or desirable to add this course: Needed for new program/concentration/certificate
  26. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service? 50 students year
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? No
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) Ph.D. in Statistics or related field.
  29. Objectives: 1 To learn methods and concepts related to statistical modeling and statistical data mining 2 To understand when to use a particular method and to learn about its limitations 3 To implement methods and concepts on real data using state of the art software
  30. Learning Outcomes: 1 To learn methods and concepts related to statistical modeling and statistical data mining 2 To understand when to use a particular method and to learn about its limitations 3 To implement methods and concepts on real data using state of the art software
  31. Major Topics: Introduction Regression Basics Statistical Inference for Regression Deviations from the linear model and modeling alternatives Collinearity Principal Components and Variable Selection Flexible methods for large data sets Nonparametric Regression and Regression Trees Methods for Time and Spatially Dependent Data Methods for combinations of cross sectionaql and time or spatially dependent data Functional Data Analysis Methods for non continuous response data Logit and Choice Models Methods for hierarchical data Linear Mixed Models Hierarchical Models
  32. Textbooks: Jank Business Analytics for Managers Springer 2011 ISBN 978 1 4614 0405 7
  33. Course Readings, Online Resources, and Other Purchases: Select readings on USF Blackboard
  34. Student Expectations/Requirements and Grading Policy: Weekly Homework 50 Class Participation 10 Data MiningProjects 30 End of Semester Presentation 10
  35. Assignments, Exams and Tests: Weekly Homework 50 Class Participation 10 Data MiningProjects 30 End of Semester Presentation 10
  36. Attendance Policy: Course Attendance at First Class Meeting Policy for Graduate Students For structured courses 6000 and above the College Campus Dean will set the first day class attendance requirement Check with the College for specific information This policy is not applicable to courses in the following categories Educational Outreach Open University TV FEEDS Program Community Experiential Learning CEL Cooperative Education Training and courses that do not have regularly scheduled meeting days times such as directed reading research or study individual research thesis dissertation internship practica etc Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty See USF Regulation Registration 4 0101 http usfweb2 usf edu usfgc ogc 20web currentreg htm Attendance Policy for the Observance of Religious Days by Students In accordance with Sections 1006 53 and 1001 74 10 g Florida Statutes and Board of Governors Regulation 6C 6 0115 the University of South Florida University USF has established the following policy regarding religious observances http usfweb2 usf edu usfgc gc pp acadaf gc10 045 htm In the event of an emergency it may be necessary for USF to suspend normal operations During this time USF may opt to continue delivery of instruction through methods that include but are not limited to Blackboard Elluminate Skype and email messaging and or an alternate schedule It s the responsibility of the student to monitor Blackboard site for each class for course specific communication and the main USF College and department websites emails and MoBull messages for important general information
  37. Policy on Make-up Work:
  38. Program This Course Supports: MS/MIS
  39. Course Concurrence Information: MBA, MS in Finance, MS in Marketing (as possible elective)


- if you have questions about any of these fields, please contact chinescobb@grad.usf.edu or joe@grad.usf.edu.