Data Analysis Using SQL and Excel
John Wiley & Sons, 2008 - 645 pagina's
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:
The companion Web site provides the data sets, Excel spreadsheets, and examples featured in the book.
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A Data Miner Looks at SQL
Whats In a Table? Getting Started with Data Exploration
How Different Is Different?
How Different Are the Averages?
Where Is It All Happening? Location Location Location
Its a Matter of Time
How Long Will Customers Last? Survival Analysis
Whats in a Shopping Cart? Market Basket Analysis
Data Mining Models in SQL
Linear Regression Models
Measuring Goodness of Fit Using R2
Weighted Linear Regression
More Than One Input Variable
Building Customer Signatures for Further Analysis