Data Analysis Using SQL and Excel

Beschreibung

Leverage the power of SQL and Excel to perform business analysis Three key efforts are essential to effectively transform data into actionable information: retrieving data with SQL, presenting data with Excel, and understanding statistics as the foundation of data analysis. Data mining expert Gordon Linoff focuses on these topics and shows you how SQL and Excel can be used to extract business information from relational databases. He begins by taking a look at how data is central to the task of understanding customers, products, and markets, and he then goes on to show you how to use that data to define business dimensions, store transactions about customers, and summarize important data to produce results. Along the way, he shares stories based on his personal experience in the field, intended to enrich your understanding of why some things work--and others don't. 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 you can expect the results to look like. Throughout the book, critical features of Excel are highlighted, interesting uses of Excel graphics are explained, and dataflows and graphical representations of data processing are used to illustrate how SQL works. Data Analysis Using SQL and Excel shares hints, warnings, and technical asides about Excel, SQL, and data analysis/mining. The book also discusses: * How entity-relationship diagrams describe the structure of data * Ways to use SQL to generate SQL queries * Descriptive statistics, such as averages, p-values, and the chi-square test * How to incorporate geographic information into data analysis * Basic ideas of hazard probabilities and survival * How data structures summarize what a customer looks like at a specific point in time * Several variants of linear regression The companion Web site provides the data sets, Excel spreadsheets, and examples featured in the book.
Leverage the power of SQL and Excel to perform business analysis Three key efforts are essential to effectively transform data into actionable information: retrieving data with SQL, presenting data with Excel, and understanding statistics as the foundation of data analysis. Data mining expert Gordon Linoff focuses on these topics and shows you how SQL and Excel can be used to extract business information from relational databases. He begins by taking a look at how data is central to the task of understanding customers, products, and markets, and he then goes on to show you how to use that data to define business dimensions, store transactions about customers, and summarize important data to produce results. Along the way, he shares stories based on his personal experience in the field, intended to enrich your understanding of why some things work--and others don't. 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 you can expect the results to look like. Throughout the book, critical features of Excel are highlighted, interesting uses of Excel graphics are explained, and dataflows and graphical representations of data processing are used to illustrate how SQL works. Data Analysis Using SQL and Excel shares hints, warnings, and technical asides about Excel, SQL, and data analysis/mining. The book also discusses: * How entity-relationship diagrams describe the structure of data * Ways to use SQL to generate SQL queries * Descriptive statistics, such as averages, p-values, and the chi-square test * How to incorporate geographic information into data analysis * Basic ideas of hazard probabilities and survival * How data structures summarize what a customer looks like at a specific point in time * Several variants of linear regression The companion Web site provides the data sets, Excel spreadsheets, and examples featured in the book.

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Gordon S. Linoff

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Computer Science Database & Data Warehousing Technologies Datenanalyse Datenbanken u. Data Warehousing Informatik

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