Data Analysis with SQL and Excel shows business managers and data analysts how to use the relatively simple tools of SQL and Excel to extract useful business information from relational databases. Each chapter explains why and when to perform a particular type of business analysis to obtain a useful business result; how to design and perform the analysis using SQL and Excel; and what the results look like in SQL and Excel. The book is full of examples using real business data. The datasets used in the book are made available on the companion web site so readers can execute the many SQL code examples provided and perform further exploration on their own.

The second edition of this favorite introduction to data mining has been extensively revised, expaned, and updated. Covers all major data mining techniques including neural networks, decision trees, clustering, association rules, genetic algorithms, memory-based reasoning and other nearest-neighbor techniques, and survival analysis. The techniques are described in clear, simple language and are illustrated with examples taken from actual data mining engagements performed by the authors.

We wish the author didn't feel he had to make up a silly new term for data mining and data miners, but we forgive him since he turns data mining into a heroic activity that is changing society for the better.

Like many business books, this one uses more pages than necessary to make its point, but there are some good examples of data mining's impact on the internal and external business processes at a variety of well-known companies.