Data Mining for Business Intelligence
                       Professor Maytal Saar-Tsechansky

           Thursdays, 5:30p...
Software

For hands-on work we will use WEKA -- an open-source award-winning Data Mining
software. The WEKA web site inclu...
McCombs Classroom Professionalism Policy
The highest professional standards are expected of all members of the McCombs com...
Tentative Course Schedule

Date          Topic                                                               Text
Thursday...
Thursday,   Market Basket Analysis: Association Rules and Sequential   Pages 287-315
March 19    Patterns.

            WE...
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Data Mining for Business Intelligence

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Data Mining for Business Intelligence

  1. 1. Data Mining for Business Intelligence Professor Maytal Saar-Tsechansky Thursdays, 5:30pm-9:30pm (Unique: 03765) GSB 3.130 Course Overview In virtually every industry, the competitive strategies organizations are employing today rely extensively on data analysis to predict the consequences of alternative courses of action, and to guide executive decision making, more generally. Companies today are competing on analytical capabilities and require analysts and decision makers who both understand the value of analytics, can identify opportunities and know how best to apply data analytics to enhance business performance. The spreading of analytical competition spans industries, including health care, retailing, travel, entertainment, consumer good, consumer finance, and even professional sports teams. This course provides a comprehensive introduction to data mining problems and tools to enhance managerial decision making at all levels of the organization and across business units. We discuss scenarios from a variety of business disciplines, including the use of data mining to support customer relationship management (CRM) decisions, risk management, decisions in the entertainment industry, and financial trading. The course is designed specifically to allow managers understand the business intelligence tools available to them to improve decisions by deriving valuable intelligence from data. The three main goals of the course are to enable students to: 1. Approach business problems data-analytically by identifying opportunities to derive business value from data. 2. Interact competently on the topic of data-driven business intelligence (know the basics of data mining techniques and how they can be applied to extract relevant business intelligence.) 3. Acquire some hands-on experience so as to follow up on ideas or opportunities that present themselves. Reading Materials Textbook (available at the bookstore) Data Mining Techniques, Second Edition by Michael Berry and Gordon Linoff Wiley, 2004 ISBN: 0-471-47064-3 Additional reading materials will be posted on blackboard or otherwise will be distributed in class. Some documents will be available in postscript format or adobe acrobat format. One Postscript viewer is Ghost script (http://pages.cs.wisc.edu/~ghost/). To read .pdf files you need Adobe's Acroread (http://www.adobe.com/products/acrobat/index.html).
  2. 2. Software For hands-on work we will use WEKA -- an open-source award-winning Data Mining software. The WEKA web site includes software download & documentation (http://www.cs.waikato.ac.nz/ml/weka/) Office Hours: By appointment, CBA, Room 5.254 Teaching Assistant: Danxia Kong ( Course Requirements and Grading Style This is a lecture-style course, however student participation is important. Students are required to be prepared and read the material before class. Students are also required to attend all sessions and discuss with the instructor any absence from class. Assignments and Projects Each meeting you will hand-in an individual write up and/or a hands-on assignment. Answers should be well thought out and concise. Late assignments Assignments must be submitted by the due date. Turn in your assignment early if there is any uncertainty about your ability to turn it in on the due date. Assignments up to one week late will have their grade reduced by 50%. After one week, late assignments will receive no credit. Projects Throughout the second half of the semester students will be working in teams to either develop or propose solutions to business problems. Each team can chose between a hands-on project, addressing a well-defined business problem through the analysis of real business and using data mining techniques, or prepare a report addressing how decisions pertaining to a relatively broad business challenge (e.g., customer churn in the telecommunication industry) can be supported with data mining. Each team is required to hand in a brief report (80% of project grade) and prepare a short class presentation of their work (20% of project grade). Grade breakdown: 1. Involvement : 15% 2. Write-Ups: 45% 3. Team project: 40% project (80% report, 20% presentation) Communication, Text, etc. The Blackboard site for this course will contain lecture notes, reading materials, assignments, and late breaking news. It is accessible via: www.utexas.edu/cc/blackboard/ Email: maytal@mail.utexas.edu Begin subject: [DM EXEC]…
  3. 3. McCombs Classroom Professionalism Policy The highest professional standards are expected of all members of the McCombs community. The collective class reputation and the value of the Texas MBA experience hinges on this. Faculty are expected to be professional and prepared to deliver value for each and every class session. Students are expected to be professional in all respects. The Texas MBA classroom experience is enhanced when: • Students arrive on time. On time arrival ensures that classes are able to start and finish at the scheduled time. On time arrival shows respect for both fellow students and faculty and it enhances learning by reducing avoidable distractions. • Students display their name cards. This permits fellow students and faculty to learn names, enhancing opportunities for community building and evaluation of in-class contributions. • Students minimize unscheduled personal breaks. The learning environment improves when disruptions are limited. • Students are fully prepared for each class. You will learn most from this class if you work and submit homework on time, keep up with the content introduced in each session, and come prepared to class. • Students respect the views and opinions of their colleagues. Disagreement and debate are encouraged. Intolerance for the views of others is unacceptable. • Laptops are closed and put away. Except for session in which we will use the WEKA software tools, I request that laptops will remain closed and put away. When students are surfing the web, responding to e-mail, instant messaging each other, and otherwise not devoting their full attention to the topic at hand they are doing themselves and their peers a major disservice. There are often cases where learning is enhanced by the use of laptops in class. In sessions when WEKA will be used, I expected students to behaved professionally. • Phones and wireless devices are turned off. When a need to communicate with someone outside of class exists (e.g., for some medical need) please inform the professor prior to class. Your professionalism and activity in class contributes to your success in attracting the best faculty and future students to this program. Academic Dishonesty Please keep in mind the McCombs Honor System. http://mba.mccombs.utexas.edu/students/academics/honor/index.asp Students with Disabilities Upon request, the University of Texas at Austin provides appropriate academic accommodations for qualified students with disabilities. Services for Students with Disabilities (SSD) is housed in the Office of the Dean of Students, located on the fourth floor of the Student Services Building. Information on how to register, downloadable forms, including guidelines for documentation, accommodation request letters, and releases of information are available online at http://deanofstudents.utexas.edu/ssd/index.php. Please do not hesitate to contact SSD at (512) 471-6259, VP: (512) 232-2937 or via e-mail if you have any questions.
  4. 4. Tentative Course Schedule Date Topic Text Thursday, Introduction to the course, introduction to data mining. Chapters 1 & 2 January 8 Supplement reading 1. The KDD process for extracting useful knowledge from volumes of data. Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth. Communications of the ACM. Volume 39, Issue 11 (November 1996). ACM Press New York, NY, USA (URL: http://citeseer.ist.psu.edu/fayyad96kdd.html) 2. "A golden vein" The Economist, Jan 2004 (A PDF is available on Blackboard) Also recommended: 3. “Competing on Analytics” by Thomas Davenport, Don Cohen, and Al Jacobson. May 2005. (URL: http://www.babsonknowledge.org/analytics.pdf) Thursday, Classification: Recursive partitioning & Decision Trees Ch 2 pp. 39-42 January 22 (revisit) Ch. 6 pp. 165-194, 209 Thursday, Predictive modeling evaluation February 5 Thursday, Lab session - WEKA Chapter 8: pp.257- February 19 271 Instance-Based Learning: K-Nearest Neighbor Classification Algorithm & applications for personalization with Please bring your Collaborative Filtering. laptops to class. Make sure WEKA is properly installed. Thursday, Bayesian learning with applications to spam filtering Chapter 8: pp.257- March 5 271 Personalization: Clustering and Segmentation WEKA demo (time permitted) Chapter 11: 349- 365
  5. 5. Thursday, Market Basket Analysis: Association Rules and Sequential Pages 287-315 March 19 Patterns. WEKA Lab Session Thursday Genetic Algorithms Chapter 13 April 2 Thursday Final Projects presentations April 16

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