Data Analysis Using SQL and Excel (Paperback)

by Gordon S. Linoff (Author)
Key Phrases: data mining models, data exploration, null hypothesis, Shopping Cart, Where Is It All Happening, Getting Started (more...)

Editorial Reviews

Product Description
Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. 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 the results should look like.

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.

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Product Details

• Paperback: 645 pages
• Publisher: Wiley; Pap/Onl edition (October 1, 2007)
• Language: English
• ISBN-10: 0470099518
• ISBN-13: 978-0470099513
• Product Dimensions: 9 x 7.4 x 1.6 inches
• Shipping Weight: 2.2 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars See all reviews (9 customer reviews)
• Amazon.com Sales Rank: #56,212 in Books (See Bestsellers in Books)

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  #43 in	Books > Computers & Internet > Databases > Data Mining
  #68 in	Books > Computers & Internet > Microsoft > Applications > Excel
  #69 in	Books > Computers & Internet > Software > Spreadsheets

Customer Reviews

Most Helpful Customer Reviews

7 of 7 people found the following review helpful:

5.0 out of 5 stars At last .. a practical data analysis guide!, July 31, 2008

By	Hugh Ujhazy "Hugh Ujhazy" (Kuala Lumpur, Malaysia) - See all my reviews (REAL NAME)

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

9 of 10 people found the following review helpful:

5.0 out of 5 stars A "Must Have" Reference for Analysts in All Fields, January 27, 2008

By	MTB NYC "MTB NYC" (New York) - See all my reviews

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.

6 of 6 people found the following review helpful:

5.0 out of 5 stars Review from a non-statistician and business intelligence manager, June 26, 2008

By	ThomasT "Thomas" (Aurora, CO United States) - See all my reviews

"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.

Lastly, there are a few odd differences between the text and the files downloadable from the web site. Whenever I hit a snag based on the text, opening the accompanying Excel files and seeing the formulas, queries or table/graph structures resolved all issues for me.

This is a text that will always have a place on my shelves.