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Graduate Course Proposal Form Submission Detail - ISM6221
Tracking Number - 1668
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Current Status:
Approved, Permanent Archive - 2007-06-21
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Detail Information
- Date & Time Submitted: 2007-03-27
- Department: IS/DS Department
- College: BA
- Budget Account Number: 1407-000-00
- Contact Person: Joan Moceri
- Phone: 9747770
- Email: jmoceri@coba.usf.edu
- Prefix: ISM
- Number: 6221
- Full Title: Data Mining
- Credit Hours: 3
- Section Type: C -
Class Lecture (Primarily)
- Is the course title variable?: N
- Is a permit required for registration?: N
- Are the credit hours variable?: N
- Is this course repeatable?:
- If repeatable, how many times?: 0
- Abbreviated Title (30 characters maximum): Data Mining
- Course Online?: -
- Percentage Online:
- Grading Option:
R - Regular
- Prerequisites: Students should have had a database course and a statistics course.
- Corequisites:
- Course Description: This course is designed for the MS in Information Systems graduate student and interested MBA students. The course covers the rapidly evolving data mining techniques that are becoming critical for customer relationship management and other applications.
- Please briefly explain why it is necessary and/or desirable to add this course: Data mining technologies are rapidly developing and being applied to a host of business challenges in marketing, sales, customer relationship management, and to simply better understand customers. In addition, these technologies are used in many other di
- 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? This course serves as a component in the data and knowledge management course sequence in the ISDS/COBA Master of Science program. The three-course package of Advanced Database Systems, Data Warehousing, and Data Mining provide the student with detailed technical coverage of current business intelligence issues, along with the skills necessary to create an effective information infrastructure.
- Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? The Data Mining course has been offered each spring (2003-2007), a total of five times.
- What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) An instructor for Data Mining should have a good theoretical background in the various machine learning techniques covered in the course, such as decision tree induction, neural networks, and association rule mining, as well as more traditional statistics. In addition, the instructor should be familiar with one or more state-of-the-art data mining tools to deliver a hands-on, project-based experience.
- Objectives: The objectives of the course are: 1) to introduce students to rapidly developing business intelligence techniques and potential application areas, 2) review the data warehousing and information infrastructure that support these efforts, and 3) to provide hands-on experience with several of the most fundamental data mining techniques in the context of a realistic project.
- Learning Outcomes: Students who complete this course will be able to: 1) identify opportunities for data mining and business intelligence technologies, 2) be more effective team members on such projects, 3) be able to use several state-of-the-art data mining tools to develop predictive models, and 4) to assess the quality of those models.
- Major Topics: The major course topics include an overview of business intelligence technologies, a review of data warehousing infrastructures, online analytic processing, and in-depth coverage of data mining techniques, such as decision tree induction, neural networks, market basket analysis, clustering, visualization, and genetic algorithms.
- Textbooks: Required Text: M. Berry and G. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Second Edition, John Wiley & Sons, 2004.
- Course Readings, Online Resources, and Other Purchases:
- Student Expectations/Requirements and Grading Policy:
- Assignments, Exams and Tests:
- Attendance Policy:
- Policy on Make-up Work:
- Program This Course Supports:
- Course Concurrence Information:
- if you have questions about any of these fields, please contact chinescobb@grad.usf.edu or joe@grad.usf.edu.