About the Book
Data Mining for Business Intelligence arose out of a data mining course at MIT's Sloan School of Management, and was refined during its use in data mining courses at both the University of Maryland's
Robert H. Smith School of Business and statistics.com. Preparation for the course revealed that there are a number of excellent books on the business context of data mining, but
their coverage of the statistical and machine-learning algorithms that underlie data mining
is not sufficiently detailed to provide a practical guide if the instructor's goal is to equip
students with the skills and tools to implement those algorithms. On the other hand, there
are also a number of more technical books about data mining algorithms, but these are
aimed at the statistical researcher, or more advanced graduate student, and do not provide the case-oriented business focus that is successful in teaching business students.
Hence, this book is intended for the business student (and practitioner) of data mining
techniques, and its goal is threefold:
- To provide both a theoretical and practical understanding of the key methods of classification,
prediction, reduction and exploration that are at the heart of data mining;
- To provide a business decision-making context for these methods;
- Using real business cases, to illustrate the application and interpretation of these
methods.
The presentation of the cases in the book is structured so that the reader can follow along
and implement the algorithms on his or her own with a very low learning hurdle.
Just as a natural science course without a lab component would seem incomplete, a data
mining course without practical work with actual data is missing a key ingredient. The MIT
data mining course that gave rise to this book followed an introductory quantitative course
that relied on Excel – this made its practical work universally accessible. Using Excel for
data mining seemed a natural progression. An important feature of this book is the use of
Excel, an environment familiar to business analysts. All required data mining algorithms
(plus illustrative datasets) are provided in an Excel add-in, XLMiner.
While the genesis for this book lay in the need for a case-oriented guide to teaching data mining, analysts and consultants who are considering the application of data mining techniques in contexts where they are not currently in use will also find this a useful, practical guide.
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