Business Intelligence for Managers (AGSB)

May 15 (Session 10):

• Read Chapter 5 of the Olson/Shi text up to p. 85
• Overview of Data Mining Methods (MS PPT)
• k Means Demonstration (MS Excel)
• Supervised Learning and k Nearest Neighbors (MS PPT)
• k NN Demonstration (MS Excel)

May 22 (Session 11):

• Read Chapter 6 of the Olson/Shi text (pp. 99 to 119)
• Using MS Excel, perform regression on the following datasets (taken from Chapter 6 of Olson/Shi text)
  1. Time Series data
     forecast requests for weeks 41 - 60, and compare against textbook results (pp. 101-102). Use MS Excel functions.
  2. Data with multiple variables . You will need the "Analysis Toolpack" addin in MS Excel for this dataset (Tools->Addins). Compare regression results against textbook (pp.104-105)
• Naive Bayes Classifiers (MS PPT)
• Naive Bayes Demonstration (MS Excel)

May 29 (Session 12):

• Read Chapter 7 of the Olson/Shi text (pp. 122-134)
• Tanagra: a free, open-source tool for data mining
• some dummy data sets that yo may use: students sent to principal's office , financial aid applicants .
• Data sets from the text are also available

June 5 (Session 13):

• Read Chapter 8 of the Olson/Shi text
• Try decision tree methods on datasets using TANAGRA
• Supplementary slides on Decision Trees
• Exercise carried out today

June 12 (Session 14):

• Agenda for this session:
  (Chapter 9 will be skipped)
  • recap of data mining classification methods discussed
  • TANAGRA parameters for each method
  • comparison of methods thru cross-validation using TANAGRA
  • clustering methods in TANAGRA
• Your group should have a dataset for study ready by this date. Email your instructor for approval to propose that dataset. The dataset should be from a business that one of the members of your team is engaged in, although you will be allowed to get existing datsets from online sources.

June 19 (Session 15):