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Objectives  

 

bullet To develop an understanding of the various concepts and tools behind warehousing and mining data for business intelligence.
bullet To develop quantitative skills pertinent to the analysis of data from huge corporate data warehouses.

 

 

Overview

 

This course provides an overview of two of the newest and hottest technologies in the area of information science: data warehousing (DW) and data mining DM). We also plan to spend sometime on a third topic: On-Line Analytical Processing (OLAP), yet another important area of tremendous growth.

Many large companies such as American Express and Wal-mart have accumulated a great deal of data from their day to day business. DW is the technology that integrates the data collected from various sources of transaction processing systems that record day to day business. Collecting data is just the first step. Companies really want information – knowledge and insight. So, the next question is, how can one uncover patterns and relationships hidden in organizational databases? Specifically, what can they learn from the data about how to please their customers, how to target their most profitable customers, how to optimally allocate their resources and how to minimize their loss such as those incurred from fraud.

The size and complexity of data in a data warehouse, however, could be overwhelming. When there are millions of trees, how can one draw meaningful conclusion about the forest? (a quote from a Two Crows Corporations Report) That is where DM comes in. DM is a cutting edge information technology that decision-makers and analysts use to extract valuable information (e.g. patterns, trends) from large databases.

According to the latest statistics, both industries are growing at double digit rates for the past few years. DW, in particular, has turned into a billion dollar business and has stimulated strong growth in data mart – specialized DW for specific purposes.

DW, data mart, and DM have attracted a lot of attention in the business world lately because of their great potential payoff. As an example, American Express reported a 15-20% increase in credit-card purchase after using DM to improve upon targeting its market. Because Fortune 500 companies are investing heavily in this technology and smaller companies such as restaurant chains are beginning to catch up, we expect employment opportunities for students who have backgrounds in DW and DM to be strong in the next five years.

 

 

 

The following is a sample of issues that I will try to address in this course. At the end of the course, you should have a framework for understanding them.

bullet what is a data warehouse, its design and function
bullet what is a data mart , its increasing use in DW
bullet what is the relationship between DW and DSS
bullet what is a multidimensional database
bullet how does the latest database technology OLAP work
bullet how DM enhances decision support systems
bullet which DM tool is appropriate for a particular business application
bullet how market can be segmented via unsupervised learning
bullet how loan officers make their decision using supervised learning programs
bullet how decision trees can help managers identify their prospective customers
bullet how you can use the latest visualization software to present your complex analysis results without showing tons of numbers
bullet how can neural networks "learn" from examples and help managers make intelligent business decisions

 

 

This class is designed in such a way that only limited mathematical background is required. Learning and understanding underlying DW concepts, studying cases, applying DM ideas and methods to business data, and communicating ideas and solutions will be our main theme. Technical details of selected DM methods will be discussed. Students are expected to try out new software for various business applications. A project is required for this class

 

 

 

 

 

 

 

 

 

 

 

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This page was last updated on : Tuesday, 30. March 2004