Data Analysis Using SQL and Excel [Englisch] [Taschenbuch]

"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.

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 field looks much richer when you realize that from zip code you can get to latitude and longitude and from latitude and longitude you can get to distance. It looks richer still when your realize that you can use it to join in census bureau data to get at important attributes such as population density, median income, percentage of people on public assistance, and the like.

* How to take advantage of dates. Order dates, ship dates, enrollment dates, birth dates. Corporate data is full of dates. These fields look richer when you understand how to turn dates into tenures, analyze purchases by day of week, and track trends in fulfillment time. They look richer still when you know how to use this data to analyze time-to-event problems such as time to next purchase or expected remaining life time of a customer relationship.

* How to build data mining models directly in SQL. This book shows you how to do things in SQL that you probably never imagined possible including generating association rules for market basket analysis, building regression models, and implementing naïve Bayes classifiers and scorecards.

* How to prepare data for use with data mining tools. Although more than most people realize can be done using just SQL and Excel, eventually you will want to use more specialized data mining tools. These tools need data in a specific format known as a customer signature. This book shows you how to create these data mining extracts.

The book is rich in examples and they all use real data. This point is worth saying more about. Unrealistic datasets lead to unrealistic results. This is frustrating to the student. In real life, the more you know about the business context, the better your data mining results will be. Subject matter expertise gives you a head start. You know what variables ought to be predictive and have good ideas about new ones to derive. Fake data does not reward these good ideas because patterns that should be in the data are missing and patterns that shouldn't be there have been introduced inadvertently. Real data is hard to come by, not least because real data may reveal more than its owners are willing to share about their business operations. As a result, many books and courses make do with artificially constructed datasets. Best of all, the datasets used in the book are all available for download at the companion web site http://www.data-miners.com/sql_companion.htm.

I reviewed the chapters of this book as they were written. This process was very beneficial to my own use of SQL and Excel. The exercise of thinking about the fairly complex queries used in the examples greatly increased my understanding of how SQL actually works. As a result, I have lost my fear of nested queries, multi-way joins, giant case statements, and other formerly daunting aspects of the language. In well over a decade of collaboration, I have always turned to Gordon for help using SQL to best advantage. Now, I can turn to this book. And you can too.

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.