Data Mining In Excel: Lecture Notes and Cases (2005) by Galit Shmueli, Nitin R. Patel, and Peter C. Bruce.
From the introduction:
This book arose out of a data mining course at MIT’s Sloan School of Management. 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.
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. XLMiner offers a variety of data mining tools: neural nets, classification and regression trees, k-nearest neighbor classification, naive Bayes, logistic regression, multiple linear regression, and discriminant analysis, all for predictive modeling. It provides for automatic partitioning of data into training, validation and test samples, and for the deployment of the model to new data. It also offers association rules, principal components analysis, k-means clustering and hierarchical clustering, as well as visualization tools, and data handling utilities. With its short learning curve, affordable price, and reliance on the familiar Excel platform, it is an ideal companion to a book on data mining for the business student.
Some what dated but remember there are lots of older copies of MS Office around. Not an inconsiderable market if you start to write something on using Excel to produce topic maps. Write for the latest version but I would have a version keyed to earlier versions of Excel as well.
I first saw this at KDNuggets.