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  • 1. MGT/P 296-2 Business Intelligence Technologies – Data Mining Spring 2006 University of California, Davis Graduate School of Management Professor Yinghui (Catherine) Yang Room 145, AOB IV, UC Davis 530-754-5967 yiyang@ucdavis.edu http://faculty.gsm.ucdavis.edu/~yiyang MGT 287: Mondays 9:00 – 11:50 am, 261 AOB IV MGP 287: Mondays 6:00 – 9:00 pm, OCM 3 Course Description Data is a key source of intelligence and competitive advantage for business organizations. With the explosion of electronic data available to organizations and the demand for better and faster decisions, the role of data driven intelligence is becoming central in organizations. Data mining is the process of converting the raw data into useful knowledge required to support decision-making. It automates the process of knowledge discovery, making us orders of magnitude more productive in our search for useful information than we would be otherwise. It also increases the confidence with which we can make business decisions. Virtually every business organization these days is in the process of exploring and implementing data mining solutions to core business problems. This course is essential for anyone interested in understanding how to get the maximum value from data, especially when abundant data are available. Application areas covered in this course include marketing, customer relationship management, financial forecasting, risk management, personalization, Web searching, etc. The course focuses on two subjects simultaneously: 1. The essential data mining and knowledge representation techniques used to extract intelligence from data and experts. Such techniques include decision trees, association rule discovery, clustering, classification, neural networks, nearest neighbor, link analysis, etc. 2. Common problems from Marketing, Finance, and Operations that demonstrate the use of various techniques. The course is structured so that it is suitable both for students interested in a conceptual understanding of data mining and its potential as well as those interested in understanding the details and acquiring hands-on skills.
  • 2. Intended Audience and Prerequisites The course is recommended for students interested in understanding the techniques and applications of data mining for making intelligent business decisions in data-rich organizations. Students interested in Marketing or Finance will also find this course useful. No prior knowledge is required for taking this course. MGT/MGP 207 or MGT/MGP 287 are not prerequisites of this course. Textbook: Text Book: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Second Edition Michael Berry and Gordon Linoff, 2004, Wiley, ISBN 0471-470643 Amazon Price: $31.50 Software: We’ll use the data mining package XLMiner, an easy-to-use comprehensive data mining add-in for Microsoft Excel. No programming skill is needed. You’ll have no difficulty using it as long as you have basic Excel skills. After you enroll in this course, I’ll provide you with a url where you can pay for and download a 6-month student copy of XLMiner at $49. The complete user guide is provided in electronic form. If you want to try out this software first, you can download a 30-day trial version at www.xlminer.com Tutorial of the software is available at http://www.resample.com/xlminer/help . Course Web site: http://faculty.gsm.ucdavis.edu/~yiyang/teaching/296spr06/296spr06.html We will make extensive use of the Web site in this course. Important information for the class (e.g., announcements, lecture notes and other handouts) will appear on the Web site. You should make a habit of checking it regularly. Note: This syllabus may change during the quarter. The course Web site will always have the most up-to-date syllabus.
  • 3. Office Hours: Knock on my door, or by appointment. Class email lists: MGT: mgt296-2-s06@ucdavis.edu MGP: mgp296-s06@ucdavis.edu You can email to the entire class if you have anything you want to address the entire class (e.g. looking for a group member). (Note: My email is also on both of the email lists.) Grading: (Subject to change) Components Grades Class Participation 10% Homework 30% Mid-Term 30% Term Project 30% Term Project: It is intended to provide you with valuable hands on experience in designing and implementing a real world data mining application, and as such, you are encouraged to develop such a system to address managerial issues you face at your work place. The project can be done individually or in groups of 2. See the project description (a separate file) for more details. Homework: There are 6 homework assignments in total. Each counts 5% of the total grades. These are very small homework assignments. Each assignment should take little time. They are used to make sure that you catch up with the class materials. You should work on them individually. Midterm: The midterm will be in-class. All lectures before the midterm are covered in the midterm. It will last 2 – 21/2 hours. You should bring a calculator. Policies:
  • 4. I’ll try my best to create a healthy learning environment both in the classroom and after class. Non-class related activities are discouraged in class. Please try your best to be on time for the class. After a class, you are responsible for reviewing the materials covered and reading the related text before the next class. Attendance: Attendance is required for this course. Class Schedule: (Subject to change) Week Topic Reading 1 (Apr 3) Course Overview, Intro to Data Mining Syllabus & Chapter 1, 2, 3 2 (Apr 10) Market Basket Analysis & Association Rules, CRM Chapter 4, 9 3 (Apr 17) Market Segmentation & Clustering, Prepare data Chapter 11, 17 4 (Apr 24) Prediction & Classification – Decision Tree Chapter 6 5 (May 1) Personalization & Nearest Neighbor Chapter 8, 12 6 (May 8) Financial Forecasting & Neural Networks Chapter 7 7 (May 15) Midterm 8 (May 22) Link Analysis & Web mining Chapter 10 9 (May 31) Guest Speaker or Misc. Topics Chapter 14, 16, 18 10 (June 5) Term project presentations Note: Business applications, cases, software demonstrations and short student presentations will be blended into each lecture. Week Handouts Due Homework 1 out, 1 (Apr 3) Project description out 2 (Apr 10) Homework 2 out Homework 1 due Homework 2 due, 3 (Apr 17) Homework 3 out Grouping Decision due 4 (Apr 24) Homework 4 out Homework 3 due Homework 4 due, 5 (May 1) Homework 5 out Project Phase 1 due 6 (May 8) Homework 5 due 7 (May 15) Midterm 8 (May 22) Project Phase 2 due 9 (May 31) Homework 6 out Homework 6 due, Project Phase 2 due, 10 (June 5) Class presentation Note: These are very small homework assignments. Each assignment should take little time. They are used to make sure that you catch up with the class materials.