Hong Zhou - Learn Data Mining Through Excel: A Step-by-step Approach for Understanding Machine Learning Methods

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484259818 . For more detailed information, please visit http://www.apress.com/source-code .

If you are a beginner in data mining, or you are a visual learner, or you want to understand the mathematical background behind some popular data mining techniques, or you are an educator, or simply because you want to improve your Excel skills, this book is right for you, and it probably is the first book you should read before you start your data mining journey. Why?

It is true that there are several outstanding data mining and machine learning software tools such as RapidMiner and Tableau that make the mining process easy and straightforward. In addition, programming languages including Python and R have many packages that can carry out most data mining tasks. However, they all hide critical aspects of the model construction process from the users, which is not helpful for beginners, visual learners, or those who want to understand how the mining process works.

Excel allows you to work with data in a transparent manner. When you open an Excel file, the data is visible immediately and you can work with the data directly. Intermediate results are contained in the Excel worksheet and can be examined. As the examination requires a thorough understanding of the data mining mechanism, learning data mining through Excel not only presents you valuable hands-on experience but also promotes your mathematical understanding of the mining mechanism.

This book explains popular data mining methods using examples in Excel. It starts with some necessary Excel skills in the first chapter and then introduces linear regression as the first data mining method, followed by k-means clustering, linear discriminant analysis, cross-validation, logistic regression, k-nearest neighbors, nave Bayes classification, decision trees, association analysis, artificial neural network, and text mining. The book ends with a summary chapter. In each chapter except for the last one, fundamental mathematical knowledge regarding each mining method is explained first, followed by Excel examples (available at https://github.com/hhohho/Learn-Data-Mining-through-Excel ) and step-by-step instructions showing how to complete the data mining task. Each chapter ends with a Review Points section that highlights the skills introduced in the chapter.

As an experienced educator, I realize that students can better develop a deep understanding of data mining methods if these methods are also explained through step-by-step instructions in Excel. I believe this book can offer you a solid understanding of the mechanics of data mining. Once you have finished this book, you should congratulate yourself that you have already become a data scientist.

First of all, this book would not be possible without the assistance from the Apress editing team including Joan Murray and Jill Balzano. Many thanks go to them. I would like to thank the technical reviewer Adam Gladstone who thoroughly examined the book for accuracy. I would also like to thank my son Michael Zhou who reviewed the book for me upon my request. In addition, I must thank my colleague Joseph Manthey who encouraged me to write this book from the beginning.