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Data Analysis Using SQL and Excel

Data Analysis Using SQL and Excel - Second Edition

By : Gordon S. S. Linoff
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Data Analysis Using SQL and Excel

Data Analysis Using SQL and Excel

By: Gordon S. S. Linoff

Overview of this book

Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS.
Table of Contents (18 chapters)
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1
Foreword
17
EULA

Lessons Learned

When analytic needs go beyond the capabilities of SQL and Excel, customer signatures can be used to store summarized behavior and demographic information for other tools. SQL has an advantage for building customer signatures because it has powerful and scalable data manipulation capabilities—and databases often contain the data that is ultimately going to be scored.

The customer signature should be based on a cutoff date, only incorporating input columns from before the date. For predictive modeling, the targets come from a time frame after the cutoff date. A customer signature has columns coming from many different tables. Most columns are input columns. The customer signature might also include target columns, identification columns, and the cutoff date.

Creating customer signatures requires gathering information from different sources. Some columns might be copied directly. Others might come from fixed lookup tables. Yet others might come from dynamic lookup...

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Data Analysis Using SQL and Excel
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