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Data Analysis Using SQL and Excel 1st Edition

4.6 out of 5 stars 31 customer reviews
ISBN-13: 978-0470099513
ISBN-10: 0470099518
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Editorial Reviews

From the Back Cover

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.

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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Product Details

  • Paperback: 645 pages
  • Publisher: Wiley; 1 edition (October 1, 2007)
  • Language: English
  • ISBN-10: 0470099518
  • ISBN-13: 978-0470099513
  • Product Dimensions: 7.4 x 1.5 x 9.2 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (31 customer reviews)
  • Amazon Best Sellers Rank: #319,187 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Paperback
"Data Analysis Using SQL and Excel" is an valuable resource for business intelligence and data mining practitioners in all industries. Having said that, I would like to offer some solid practical advice to potential readers that might not be fluent in statistics or data mining.

First, the reader should have a solid understanding of SQL. If the extent of your SQL interaction comes through a program on the level of Access, then you can still benefit from this book, but you will have to apply yourself more than others. Keep in mind, that proprietary releases of SQL might cause problems in directly translating the author's examples.

Second, if your statistics knowledge is a little rusty, have a secondary resource on-hand. Sometimes the definitions or explanations of the statistical concepts may not be as intuitive for some readers as they are for others.

With those caveats in mind, the reader need only to keep his or her patience and work through the concepts of the first 4-5 chapters. These chapters tend toward simple exposition of the concepts. For those with little patience, it may seem as if it is just a laundry list of concepts with little effort to tie those concepts into practical uses. Thinking like this is a great way to miss the enormous benefits of the book!

For me, the "Ah Ha!" moment came in Chapter 6 and 7. The concepts I had worked on in the previous chapters suddenly came together with customer tenure onward, when the techniques use will call to mind everything learned in the previous chapters.

In short, spend plenty of time in the first few chapters - the extra effort to master those concepts will only enhance the benefits of later chapters.
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Format: Paperback
Unlike textbooks related to stats and data analysis, this practical, "easy to read" book actually bridges the gap between theory and practice. The reader will understand both the "how" and "why" behind common approaches to data analysis. Best of all, the book targets a general audience and avoids intimidating language and notations. The author tackles the most common statistical concepts with colorful vinets. In fact, the explanations behind such ideas as "degrees of freedom" and "chi-square" are the clearest that I have ever seen in any reference or textbook.

Bototm line: whether you are a seasoned expert or novice, this is an invaluable, practical guide that will provide quick answers for anyone needing to analyze data using Excel.
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Format: Paperback
Having seen a multitude of books offering either statistical analysis techniques or suggestions around data mining tools, it is refreshing to see someone approach the subject using simple, readily available tools and a practical, business oriented approach to the topic. The apparently mundane subject of customer retention coupled with buying patterns and market basket analysis is laid out in an effective and sequential manner. The SQL examples take some getting used to but, once understood, offer a series of easily implemented and highly effective methods to illustrate the concepts shown in the book. As a reference guide and an illustration that one needs to know the questions to be asked of the data before investing in the latest drag and drop business intelligence tools, this book is unparalleled. The author has not stinted on providing a wealth of examples and explanation. If this tome is a reflection of how Mr Linoff and his team approach their real world consulting activities, they must be a formidable team indeed.

For anyone who has wrestled with a means to understand their customer buying patterns and product affinity patterns in their historical sales data, this book cannot be beaten
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Format: Paperback Verified Purchase
Gordon Linoff and I have written three an a half books together. (Four, if we get to count the second edition of Data Mining Techniques as a whole new book; it didn't feel like any less work.) Neither of us has written a book without the other before, so I must admit to a tiny twinge of regret upon first seeing the cover of this one without my name on it next to Gordon's. The feeling passed very quickly as recollections of the authorial life came flooding back--vacations spent at the keyboard instead of in or on the lake, opportunities missed, relationships strained. More importantly, this is a book that only Gordon Linoff could have written. His unique combination of talents and experiences informs every chapter.

I first met Gordon at Thinking Machines Corporation, a now long-defunct manufacturer of parallel supercomputers where we both worked in the late eighties and early nineties. Among other roles, Gordon managed the implementation of a parallel relational database designed to support complex analytical queries on very large databases. The design point for this database was radically different from other relational database systems available at the time in that no trade-offs were made to support transaction processing. The requirements for a system designed to quickly retrieve or update a single record are quite different from the requirements for a system to scan and join huge tables. Jettisoning the requirement to support transaction processing made for a cleaner, more efficient database for analytical processing. This part of Gordon's background means he understands SQL for data analysis literally from the inside out.
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