So Much Data So Little Math is geared for the business community and for employees who are trying to interpret statistics without a background in formal mathematics. The book, relying on the availability of modern software rather than on the reader's math skills, puts trend analysis within reach of everyone. Through a simple method centering on case studies of ordinary business owners, May provides a simple way to learn how to model data. This highly practical text teaches five methods of modeling: regression, correlation, spreadsheets, database systems and neural networks. A must-read for any successful entrepreneur, So Much Data So Little Math will also be of great interest to researchers, business students and their instructors.
William D. May is a Senior Scientist with the Central Intelligence Agency and an Adjunct Professor of Computer Science at Virginia Tech.
Chapter 1 Acknowledgements Chapter 2 Preface Chapter 3 Data Analysis Chapter 4 From Data to Model, From Model to Plan Chapter 5 Levels of Data Analysis Chapter 6 Math Anxiety Chapter 7 Statistics Chapter 8 The Essential Spreadsheet Chapter 9 Calculus Chapter 10 Neural Networks for Beginners Chapter 11 Basic Logic Chapter 12 Mining Databases Chapter 13 Lost in a Large Corporation Chapter 14 Real-World Applications Chapter 15 Bibliography Chapter 16 Index Chapter 17 Author Biography