Data Analysis Using SQL and Excel

Author:   Gordon S. Linoff
Publisher:   John Wiley and Sons Ltd
ISBN:  

9780470099513


Pages:   696
Publication Date:   16 October 2007
Format:   Paperback
Availability:   In Print   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

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Data Analysis Using SQL and Excel


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Full Product Details

Author:   Gordon S. Linoff
Publisher:   John Wiley and Sons Ltd
Imprint:   John Wiley & Sons Ltd
Dimensions:   Width: 18.80cm , Height: 3.80cm , Length: 23.50cm
Weight:   0.001kg
ISBN:  

9780470099513


ISBN 10:   0470099518
Pages:   696
Publication Date:   16 October 2007
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Out of Print
Availability:   In Print   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

Foreword. Acknowledgments. Introduction. Chapter 1 A Data Miner Looks at SQL. Chapter 2 What?s In a Table? Getting Started with Data Exploration. Chapter 3 How Different Is Different? Chapter 4 Where Is It All Happening? Location, Location, Location. Chapter 5 It?s a Matter of Time. Chapter 6 How Long Will Customers Last? Survival Analysis to Understand Customers and Their Value. Chapter 7 Factors Affecting Survival: The What and Why of Customer Tenure. Chapter 8 Customer Purchases and Other Repeated Events. Chapter 9 What?s in a Shopping Cart? Market Basket Analysis and Association Rules. Chapter 10 Data Mining Models in SQL. Chapter 11 The Best-Fit Line: Linear Regression Models. Chapter 12 Building Customer Signatures for Further Analysis. Appendix Equivalent Constructs Among Databases. Index.

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Author Information

GORDON S. LINOFF is a cofounder of Data Miners, Inc., a consultancy specializing in data mining. He is the coauthor of the bestselling Data Mining Techniques, Second Edition, and Mastering Data Mining (both from Wiley). He has more than a decade of experience applying data mining techniques to business problems in marketing and customer relationship management.

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