Data Analysis Using SQL and Excel

by Gordon S. Linoff

Book Overview

Die zweite Auflage von Data Analysis Using SQL and Excel zeigt, wie man diese beiden beliebten Werkzeuge der Datenabfrage und -analyse am besten nutzt, um anspruchsvolle Datenanalysen ohne komplexe,... This description may be from another edition of this product.

• Format: Paperback
• Language: English
• ISBN: 0470099518
• ISBN13: 9780470099513
• Release Date: October 2007
• Publisher: Wiley
• Length: 645 Pages
• Weight: 0.05 lbs.
• Dimensions: 1.5" x 7.4" x 9.2"

Customer Reviews

great book about excel, statistics & sql

I have been teaching introductory and intermediate statistics to public policy graduate students for the past ten years. I also work in the media industry where (very) large data needs to be analyzed daily. This is one of the books that I highly recommend to my students and colleagues, because it recognizes a reality in the world: most data is stored in relational databases and most analysis is in Excel. This book is excellent, so you'll need two copies... Someone is always borrowing mine.

At last .. a practical data analysis guide!

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

Review from a non-statistician and business intelligence manager

"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.

A "Must Have" Reference for Analysts in All Fields

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.

Comments from a colleague

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. Just as a database designed to answer big important questions has a different structure from one designed to process many individual transactions, a book about using databases to answer big important questions requires a different approach to SQL. Many books on SQL are written for database administrators. Others are written for users wishing to prepare simple reports. Still others attempt to introduce some particular dialect of SQL in every detail. This one is written for data analysts, data miners, and anyone who wants to extract maximum information value from large corporate databases. Jettisoning the requirement to address all the disparate types of database user makes this a better, more focused book for the intended audience. In short, this is a book about how to use databases the way we ourselves use them. Even more important than Gordon's database technology background, is his many years as a data mining consultant. This has given him a deep understanding of the kinds of questions businesses need to ask and of the data they are likely to have available to answer them. Years spent exploring corporate databases has given Gordon an intuitive feel for how to approach the kinds of problems that crop up time and again across many different business domains: * How to take advantage of geographic data. A zip code fiel