IDS572 Class1_ notes82511

IDS572 Class1_ notes82511 - Data Mining for Business UIC...

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1 Data Mining for Business Session 1 Introduction to Data Mining Kenneth Stehlik-Barry IBM UIC IDS572 August 25, 2011
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2 Session 1 Tonight's Agenda Introductions Course Overview Introduction to Data Mining Discussion
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3 Session 1 Faculty Kenneth Stehlik-Barry Principal Consultant, SPSS and IBM @SPSS/IBM 31 Years
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4 Session 1 Students Name Organization (if applicable) Major Experience/Interest in Data Mining Familiarity with analytic software (SPSS, SAS, R, etc.) Your objectives
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5 Session 1 Course Overview Most data mining courses offered today are from Schools of Computer Science or Mathematics. This course is designed for IT and business professionals. Course covers data mining process and introduces the mostly commonly employed techniques
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6 Session 1 Syllabus Description Objectives Approach Grading Materials Schedule Project Tools
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7 Session 1 Introduction to Data Mining Part I
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8 Session 1 Data Mining Definition Increasingly described as Predictive Analytics The process of finding patterns in data. The process of using automated techniques to find useful and previously unknown patterns in large volumes of data for the purpose of describing and predicting. or
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9 Session 1 What is Data Mining? Can you find the meaningful pattern in this data?
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10 Session 1 Data Mining is vs. is not… Is A user-centric, interactive process which leverages analysis technologies and computing power Focused on solving a problem or addressing a challenge “Computers and algorithms don’t mine data; people do!” Is not Blind application of analysis/modeling algorithms Brute-force crunching of bulk data
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11 Session 1 Why Data Mining in business? How often do our best customers buy? What motivates customers to make multiple purchases? How can we ensure long-term loyalty? How do we attract and retain new customers? How can we personalize and align offers to achieve maximum ROI? How can we detect fraud?
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12 Session 1 Impact of Data Mining Standard Life secured 50 million in mortgage revenue Verizon Wireless retained 33% of targeted customers, reduced direct mail budget by 60% and increased usage and revenue Data mining is a way to lift CRM projects into a higher level of return on investment.”
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13 Session 1 Knowledge Gap Quantity of Data 1960 1970 1980 1990 2000 2010 Execution Capability Analytic Capability Available Data Knowledge Gap Execution Gap Source: Gartner Group
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14 Session 1 Part II Data Mining Objectives
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15 Session 1 Data Mining Objectives Classification Prediction Clustering Association Categories of Techniques Inferential vs. Non-Inferential
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16 Session 1 Classification Identify characteristics that define and differentiate known groups ü What defines good candidates for credit cards?
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This note was uploaded on 11/01/2011 for the course IDS 572 taught by Professor Staff during the Fall '08 term at Ill. Chicago.

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IDS572 Class1_ notes82511 - Data Mining for Business UIC...

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