Form 2B City University of Hong Kong Information on a Course offered by the Department of Management Sciences with effect from Semester A in 2012 / 2013 Part I Course Title: Data Mining Course Code: MS6711 Course Duration: One Semester No. of Credit Units: 3 Level: P6 Medium of Instruction: English Prerequisites: Nil Precursors: MS5212 Statistical Methods I or equivalent Equivalent Courses: Nil Exclusive Courses: FB6711 Data Mining Part II Course Aims: The course aims to provide an introduction to discover hidden information from business data using data mining techniques with the aid of SAS Enterprise Miner software. 1
Course Intended Learning Outcomes (CILOs): Upon successful completion of this course, students should be able to: No. CILOs Weighting 1 Demonstrate knowledge of data mining concepts and algorithms. 20% 2 Define and formulate real-world data mining problems. 20% 3 Explore and prepare the data for data mining projects. 20% 4 Evaluate critically the appropriateness of the extracted 10% information. 5 Extract and analyze information from data with the use of SAS Enterprise Miner software. 30% Teaching and Learning Activities (TLAs): (Indicative of likely activities and tasks designed to facilitate students achievement of the CILOs. Final details will be provided to students in their first week of attendance in this course) CILO TLAs No. 4 1. Lectures Lecturer explains and discusses the concepts, algorithms, of data mining and how to evaluate the quality of the extracted information. 5 2. In-class activities Lecturer will demonstrate the use of SAS Enterprise Miner software in class on selected topics. Students are required to work individually or as a group on simulated or small real data using the software. Through these inclass exercises, the lecturer can identify the common problems that students have and give more elaboration as needed. Students can also identify the kinds of mistakes that they have made and learn how to correct them. 5 3. Out-of-class assignments Running the data mining algorithms on large data set is a very time consuming process. It is not possible to do it regularly in class. Students tackle focused problems based on large business data as out-of-class assignments. Students may work in small groups for these assignments so that they can discuss the problems and come up a solution together. 4. Project The ultimate aim of the course is to provide students with the specialist knowledge and training to run a business data mining task. Students are given a large data set with described business problem. They are asked to extract useful information related to the set of identified data mining goals for the problem. This is likely to be a semester-long activity. Students need to make use of everything they have learned in this course in order to achieve the goals. They are encouraged to form small groups for the project so that they can analyze the data and run the software together. They can always seek help and advice from the lecturer during the semester. Hours/week 2
Constructive Alignment of CILOs and TLAs TLA1 TLA2 TLA3 TLA4 CILO 1 CILO 2 CILO 3 CILO 4 CILO 5 Assessment Tasks/Activities (Indicative of likely activities and tasks designed to assess how well the students achieve the CILOs. Final details will be provided to students in their first week of attendance in this course) This course is assessed via coursework and examination. CILO NO. Assessment Tasks (ATs) 1. Assignments, or cases report Assessment Details Small student groups may be formed for these assignments, which are designed to allow students to work with large data set in their own time. Students may also be asked to read business data mining cases and prepare written or oral reports. 2. Project Students are encouraged to work together in small groups. They may be asked to submit phase by phase reports or 1 single report at the end of the semester. An oral presentation on their works may also be required. 3. Examination A formal examination. Individual work is expected. Weighting 10% 50% 40% Constructive alignment of CILOs and ATs AT 1 AT 2 AT 3 CILO 1 CILO 2 CILO 3 CILO 4 CILO 5 3
Grading of Student Achievement: Assignment, Project & Examination Grade Grade Point Grade Definitions Excellent: A+ A A- B+ B B- C+ C C- 4.3 4.0 3.7 3.3 3.0 2.7 2.3 2.0 1.7 Good: Adequate: Excellent achievement in all aspects of CILOs, including good knowledge in data mining concepts, knowing how to prepare, utilising SAS Enterprise Miner software, selecting appropriate data mining tools, judging the quality of the results, and presenting the results effectively. Good achievement in most aspects of CILOs, including good knowledge in data mining concepts, knowing how to prepare, utilising SAS Enterprise Miner software, selecting appropriate data mining tools, judging the quality of the results, and presenting the results effectively. Satisfactory achievement in most aspects of CILOs, including good knowledge in data mining concepts, knowing how to prepare, utilising SAS Enterprise Miner software, selecting appropriate data mining tools, judging the quality of the results, and presenting the results effectively. D 1.0 Marginal: Satisfactory achievement in some aspects of CILOs, but have serious weakness in one or more aspects of the CILOs. F 0.0 Failure: Failed to achieve most of the CILOs. Part III Keyword Syllabus: 1. Introduction to data mining What is data mining? Why use data mining? How do you mine data? Data mining terminology. 2. Data Mining Process Defining a study; Data preparation; Data cleansing; Building a model. 3. Data Mining Tools and Technologies Memory-based reasoning; Decision trees; Neural nets; Clustering analysis; Association rules; Statistical models. 4. Case Studies Applications in banking and finance, retail, telecommunications, and financial market. 5. Data Mining Software SAS Enterprise Miner Software. 4
Recommended Reading Berry, M & Linoff, G, Data mining techniques: For marketing, sales, and customer support, 3 nd Edition, John Wiley & Sons, 2011. Getting Started with SAS Enterprise Miner 6.1, SAS Pub., 2008. Nisbet, R, Elder, J, Miner, G, Handbook of Statistical Analysis and Data Mining Applications, Academic Press, 2009. Matignon, R, Data Mining Using SAS Enterprise Miner, Wiley-Interscience, 2007. Cerrito, P B, Introduction to Data Mining Using SAS Enterprise Miner, SAS Institute, 2006. Tufféry, S, Data Mining and Statistics for Decision Making, Wiley, 2011. Giudici, P, Applied Data Mining: Statistical Methods for Business and Industry, John Wiley & Sons, 2003. Berry, M, & Linoff, G, Mastering Data Mining, John Wiley & Sons, 2000. Cabena, P, Hadjinian, P, Stadler, R, Verhees, J, & Zanasi, A, Discovering data mining: From concept to implementation, Prentice Hall, 1998. Delmater, R, & Hancock, M, Data mining explained: A manager s guide to customer-centric business intelligence, Digital Press, 2001. Diloriom, F C & Kenneth A. Hardy, K A, Quick Start to Data Analysis with SAS, Duxbury Press, 1996. Elliott, R J, Learning SAS in the computer lab, 2nd edition, Duxbury Press, 2000. Groth, R, Data mining: A hands-on approach for business professionals, Prentice Hall, 1998. Han, J, & Kamber, M, Data mining: Concepts and techniques, Morgan Kaufmann Pub., 2000. Roiger R J & Geatz, M W, Data Mining A tutorial Based Primer, Pearson Education, 2003. Weiss, S M & Indurkhya, N, Predictive data mining A practical guide, Morgan Kaufmann Publishers, Inc., 1998 Westphal, C & Blaxton, T, Data mining solutions: Methods and tools for solving real-world problems, John Wiley & Sons, 1998. 5