City University of Hong Kong. Information on a Course offered by Department of Management Sciences with effect from Semester A in 2010 / 2011

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1 City University of Hong Kong Information on a Course offered by Department of Management Sciences with effect from Semester A in 200 / 20 Part I Course Title: Enterprise Data Mining Course Code: MS4224 Course Duration: One Semester No. of Credit Units: 3 Level: B4 Medium of Instruction: English Prerequisites: Nil Precursors: FB2200 Management Sciences I or equivalent MS327 SAS Programming or equivalent Equivalent Courses: Nil Exclusive Courses: MS432 Customer Relationship Management with Data Mining MS4424 Data Mining and Modelling Part II Course Aims: This course aims to Provide fundamental concepts and techniques of using data mining in the context of business applications.

2 Course Intended Learning Outcomes (CILOs) Upon successful completion of this course, students should be able to: No. CILOs Weighting CILO. Implement different stages of the business data mining process. CILO 2. Make use of different data mining techniques to achieve data mining goals. CILO 3 Master the SAS/Enterprise Miner software to perform data mining tasks. The above learning outcomes are equally important for this course. Teaching and learning Activities (TLAs) (Indicative of likely activities and tasks students will undertake to learn in this course. Final details will be provided to students in their first week of attendance in this course.) Including: TLA : Lectures Lecturer explains data mining concepts and techniques that are commonly used in business data mining applications, and demonstrates how to master SAS Enterprise Miner software. TLA 2: Student activities Students apply the learned concepts, techniques and SAS Enterprise Miner skills on exercise questions and project data. Constructive Alignment of ILOs and Teaching and Learning Activities TLA CILO TLA TLA 2 CILO Yes Yes CILO 2 Yes Yes CILO 3 Yes Yes 2

3 Assessment Tasks/Activities (Indicative of likely activities and tasks students will undertake to learn in this course. Final details will be provided to students in their first week of attendance in this course.) Project 50% Assignments or Tests 50% Total 00% Constructive Alignment of ILOs and Assessment Methods Assessment Methods ILO Examination Project Assignments or Tests Yes Yes Yes 2 Yes Yes Yes 3 No Yes No Assessment Weights on ILOs and Assessment Methods Assessment Methods CILO Project Assignments or Tests Total CILO and CILO Total Grading of Student Achievement: Refer to Grading of Courses in the Academic Regulations Letter Grade A+ A A- B+ B B- C+ C C- Grade Point Grade Definitions Overall score is 8 or above out of 00. Showed excellent knowledge in the concept and techniques of data mining, mastered SAS Enterprise Miner software excellently, and able to present the findings clearly. Overall score is between 66 and 80 out of 00. Showed good knowledge in the concept and techniques of data mining and mastered SAS Enterprise Miner software well. Overall score is between 5 and 65 out of 00. Showed good knowledge in the concept and techniques of data mining but could not fully master SAS Enterprise Miner software. 3

4 D.0 Overall score is between 4 and 50 out of 00. Showed some knowledge in the concept and techniques of data mining but could not fully master SAS Enterprise Miner software. F 0.0 Overall score is 40 or below out of 00. Showed insufficient understanding in the concept and techniques of data mining and could not master SAS Enterprise Miner software. Part III Keyword Syllabus: Overview of Data Mining Data mining process. Data mining techniques Data preparation; Clustering; Logistic Regression; Decision Tree; Neural Network; Association Analysis SAS Enterprise Miner Software SAS programming; SAS Enterprise Miner Software. Recommended Reading: Tan, P.N., Steinbach, M. and Kumar, V., Introduction to Data Mining. Addison Wesley, Paolo Giudici, Applied Data Mining: Statistical Methods for Business and Industry, John Wiley & Sons, Matignon, Randall. Data Mining Using SAS Enterprise Miner. Second Edition. Wiley, Cerrito, Patricia, Introduction to Data Mining Using SAS Enterprise Miner. SAS Institute, Michael Berry, & Gordon Linoff, Data mining techniques: For marketing, sales, and customer support, John Wiley & Sons, Naeem Siddiqi, Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring, John Wiley & Sons, Raymond Anderson, The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Manamgenet and Decision Automation, Oxford, 2007 Getting Started with SAS Enterprise Miner 6., SAS Pub., Patricia B. Cerrito, Introduction to Data Mining Using SAS Enterprise Miner, SAS Institute,

5 Michael Berry, & Gordon Linoff, Mastering Data Mining, John Wiley & Sons, Jiawei Han, & Micheline Kamber, Data mining: Concepts and techniques, Morgan Kaufmann Pub.,