Acknowledgments

Without a doubt, we are living in the age of data mining and Big Data analytics. Because of their popularity (and perhaps a little bit of hype), everybody is talking about data mining and Big Data analytics, often in different scope and contexts, using diverse terminology. The main goal of this book is to explain the language of analytics and data mining in a comprehensive yet not-too-technical way. If I have, at least partially, succeeded in achieving this goal, it is because of the direct and indirect contributions of a number of people.

I want to thank my colleagues and my students for providing me with the broader perspective toward analytics that I needed to write this book in a holistic manner. As is the case for most academics, I also have my own opinions and biases toward “what is what” in data mining and in analytics, and thanks to my academic friends, I think I managed to rise above them to make this book inclusive and comprehensive.

We academics tend to be focused on rigor and theory, sometimes in the process moving away from relevance and reality. Thanks to my clients and corporate partners who continuously provide me with the realities of the real world that I need to stay balanced between rigor and relevance. Writing a book titled Real-World Data Mining requires such connection to reality, without compromising technical accuracy, and I have to thank to my corporate friends for helping me achieve that in this book.

I want to thank to my publisher, Ms. Jeanne Levine, and Pearson for presenting me with the opportunity to write this book and for being patient and resourceful for me throughout the journey of actually writing it.