Explore the vast range of titles available.

A practical guide to data mining using SQL and Excel 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. -Understand core analytic techniques that work with SQL and Excel -Ensure your analytic approach gets you the results you need -Design and perform your analysis using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.

The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised—is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. * Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems * Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately * Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more * Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.  Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

A practical guide to data mining using SQL and ExcelData 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.Understand core analytic techniques that work with SQL and ExcelEnsure your analytic approach gets you the results you needDesign and perform your analysis using SQL and ExcelData Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.

A customer signature contains summarized attributes of customers, putting important information in one place, useful both for building models and for scoring them, as well as for reporting and ad hoc analyses. This chapter brings these ideas together around the concept of the customer signature, information that summarizes customers along multiple dimensions. It starts by explaining customer signatures and time frames in detail. Pivoting data summarizes transactions by aggregating information along various dimensions. Then, the chapter discusses the technical operations for building signatures, and interesting attributes to include in them. It focuses on prediction model sets because they are more powerful. The customer signature might also include target columns, identification columns, and the cutoff date. The ability of SQL to express very complex data manipulations, and optimize the resulting queries on large hardware, makes relational databases a powerful choice for creating customer signatures.

Along with geography, time is a critical dimension describing customers and businesses. This chapter introduces dates and times as tools for understanding customers. It starts with an overview of date and time data types in SQL and basic types of questions to ask about such data. The chapter continues by looking at other columns and how their values change over time, with tips on how to do year‐over‐ year comparisons. The previous year usually provides the best comparison for what is happening the following year. The chapter explains how to use comparisons by day, week, and month in scenarios where this year's data is not complete. It finishes with two useful examples. The first is determining the number of customers active at a given point in time. The second relies on simple animations in Excel to visualize changes in durations over time.

This chapter uses SQL for exploring data, the first step in any analysis project. Understanding what the data represents is a theme of this chapter. Spreadsheets, the most common data analysis tool, give users power over the data, with the ability to add columns and rows, to apply functions, to summarize, create charts, make pivot tables, and color and highlight and change fonts to get just the right look. The chapter starts by reviewing some of the charting tools in Excel, providing tips for creating good charts. The intention is to motivate good practices by explaining the reasons, not to be a comprehensive resource explaining, click‐by‐click, what to do in Excel. It continues with exploring data in a single table, column by column. The chapter ends with a method for automating some descriptive statistics for columns, touching upon string values, comparing values and summary code.

This chapter focuses on the specific products, to learn both about customers and the products they buy. Market basket analysis is the general name for understanding product purchase patterns at the customer level. The chapter starts by exploring the individual products purchased in an order, with an emphasis on exploratory data analysis. Products are related to customer attributes as well. Some products have a wide geographic distribution; others may be more narrowly focused. Some more frequently purchased, some only once, perhaps indicating a poor customer experience with the product or an opportunity to broaden the customer relationship. Pricing is a very important aspect of products, and the price for a given product can vary over time. One important type of question about the products purchased by households is set‐within‐a‐set queries. These queries can be solved using several methods, but aggregation with an intelligent HAVING clause is very versatile.

Association rules go beyond merely exploring products: They identify groups of products that tend to appear together. A big part of the allure and power of association rules is that they discover patterns automatically. This chapter introduces the techniques for discovering association rules using SQL. One advantage of using SQL for association rules is that the technique can be modified to fit particular needs. A slight variation, called sequential association rules, finds the order in which products are purchased. The traditional way of measuring the goodness of these rules is with three measures: support, confidence, and lift. A better measure, however, is based on the chi‐square value. Association rules are very powerful and extensible. Using SQL, the simple one‐way associations can be extended to two‐way rules and beyond. With a relatively simple modification, the same mechanism can generate sequential rules, where products occur in a specific order.