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Data Analysis Using SQL and Excel pdf

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Data Analysis Using SQL and Excel pdf

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Name:Data Analysis Using SQL and Excel pdf

Infohash: 2BC48731C5FF428DB03E6E5A0A9F8C8CAADE78D4

Total Size: 9.71 MB

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Last Updated: 2026-01-22 13:47:38 (Update Now)

Torrent added: 2014-05-04 09:34:26






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Data Analysis Using SQL and Excel.pdf (Size: 9.71 MB) (Files: 1)

 Data Analysis Using SQL and Excel.pdf

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ABOUT THIS BOOK
Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.

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.


ABOUT THE AUTHOR
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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