George Fernandez is a professor of applied statistical methods and the director of the Center for Research Design and Analysis at the University of Nevada in Reno.

Its key features include the provision of case studies throughout the sections, downloadable macros and instructions on how to run them. … The step-by-step instructions and the graphical representations of data make it particularly useful to those wishing to communicate complex and technical data to a largely non-specialist audiences.—Kassim S. Mwitondi, Journal of Applied Statistics, 2012Like the first edition of the book, this new edition provides a high-level introduction to some important concepts and algorithms in data mining. … the author presents broad statistical data mining solutions without writing SAS program codes. One of the nicest features of this book is that it gives access to SAS macros directly from the desktop and offers to create publication quality graphs. … this new edition provides a simple and straightforward introduction to data mining, along with a number of detailed, worked case studies.—Technometrics, February 2011Praise for the First Edition:The macros integrate nicely with SAS’s output delivery system … . this is a book that could serve as an easy-to read introduction to some classical statistical techniques that are used in data mining, and, with the associated macros, provide an opportunity to see those techniques in action.—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466Use of these data mining SAS macros facilitated reliable conversion, examination, and analysis of the data, and selection of best statistical models despite the great size of the data sets. …—Christopher Ross, US Bureau of Land ManagementAn excellent treatment of data mining using SAS applications is provided in this book. … This book would be suitable for students (as a textbook), data analysts, and experienced SAS programmers. No SAS programming experience, however, is required to benefit from the book.—Computing Reviews, June 2003… the book provides a welcome contrast to treatments of data mining that focus on only the most novel aspects of the subject. Dr. Fernandez is quite right in pointing out that a lot of data mining can be carried out by standard statistical methods in familiar packages. The book also has a healthy emphasis on the use of cross validation (a hallmark of data mining). This and other concepts are well illustrated with numerous examples. Finally, the book demonstrates that the fancy (and expensive) user interfaces sported by many data mining work benches are not essential to the data mining enterprise and might even be counterproductive.—Computational Statistics, 2005

• Data Mining: A Gentle Introduction. Preparing Data for Data Mining. Exploratory Data Analysis. Unsupervised Learning Methods. Supervised Learning Methods: Prediction. Supervised Learning Methods: Classification. Advanced Analytics and Other SAS Data Mining Resources. Appendices. Index.

George Fernandez is a professor of applied statistical methods and the director of the Center for Research Design and Analysis at the University of Nevada in Reno.

Its key features include the provision of case studies throughout the sections, downloadable macros and instructions on how to run them. … The step-by-step instructions and the graphical representations of data make it particularly useful to those wishing to communicate complex and technical data to a largely non-specialist audiences.—Kassim S. Mwitondi, Journal of Applied Statistics, 2012Like the first edition of the book, this new edition provides a high-level introduction to some important concepts and algorithms in data mining. … the author presents broad statistical data mining solutions without writing SAS program codes. One of the nicest features of this book is that it gives access to SAS macros directly from the desktop and offers to create publication quality graphs. … this new edition provides a simple and straightforward introduction to data mining, along with a number of detailed, worked case studies.—Technometrics, February 2011Praise for the First Edition:The macros integrate nicely with SAS’s output delivery system … . this is a book that could serve as an easy-to read introduction to some classical statistical techniques that are used in data mining, and, with the associated macros, provide an opportunity to see those techniques in action.—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466Use of these data mining SAS macros facilitated reliable conversion, examination, and analysis of the data, and selection of best statistical models despite the great size of the data sets. …—Christopher Ross, US Bureau of Land ManagementAn excellent treatment of data mining using SAS applications is provided in this book. … This book would be suitable for students (as a textbook), data analysts, and experienced SAS programmers. No SAS programming experience, however, is required to benefit from the book.—Computing Reviews, June 2003… the book provides a welcome contrast to treatments of data mining that focus on only the most novel aspects of the subject. Dr. Fernandez is quite right in pointing out that a lot of data mining can be carried out by standard statistical methods in familiar packages. The book also has a healthy emphasis on the use of cross validation (a hallmark of data mining). This and other concepts are well illustrated with numerous examples. Finally, the book demonstrates that the fancy (and expensive) user interfaces sported by many data mining work benches are not essential to the data mining enterprise and might even be counterproductive.—Computational Statistics, 2005

• Data Mining: A Gentle Introduction. Preparing Data for Data Mining. Exploratory Data Analysis. Unsupervised Learning Methods. Supervised Learning Methods: Prediction. Supervised Learning Methods: Classification. Advanced Analytics and Other SAS Data Mining Resources. Appendices. Index.

George Fernandez is a professor of applied statistical methods and the director of the Center for Research Design and Analysis at the University of Nevada in Reno.

Its key features include the provision of case studies throughout the sections, downloadable macros and instructions on how to run them. … The step-by-step instructions and the graphical representations of data make it particularly useful to those wishing to communicate complex and technical data to a largely non-specialist audiences.—Kassim S. Mwitondi, Journal of Applied Statistics, 2012Like the first edition of the book, this new edition provides a high-level introduction to some important concepts and algorithms in data mining. … the author presents broad statistical data mining solutions without writing SAS program codes. One of the nicest features of this book is that it gives access to SAS macros directly from the desktop and offers to create publication quality graphs. … this new edition provides a simple and straightforward introduction to data mining, along with a number of detailed, worked case studies.—Technometrics, February 2011Praise for the First Edition:The macros integrate nicely with SAS’s output delivery system … . this is a book that could serve as an easy-to read introduction to some classical statistical techniques that are used in data mining, and, with the associated macros, provide an opportunity to see those techniques in action.—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466Use of these data mining SAS macros facilitated reliable conversion, examination, and analysis of the data, and selection of best statistical models despite the great size of the data sets. …—Christopher Ross, US Bureau of Land ManagementAn excellent treatment of data mining using SAS applications is provided in this book. … This book would be suitable for students (as a textbook), data analysts, and experienced SAS programmers. No SAS programming experience, however, is required to benefit from the book.—Computing Reviews, June 2003… the book provides a welcome contrast to treatments of data mining that focus on only the most novel aspects of the subject. Dr. Fernandez is quite right in pointing out that a lot of data mining can be carried out by standard statistical methods in familiar packages. The book also has a healthy emphasis on the use of cross validation (a hallmark of data mining). This and other concepts are well illustrated with numerous examples. Finally, the book demonstrates that the fancy (and expensive) user interfaces sported by many data mining work benches are not essential to the data mining enterprise and might even be counterproductive.—Computational Statistics, 2005

• Data Mining: A Gentle Introduction. Preparing Data for Data Mining. Exploratory Data Analysis. Unsupervised Learning Methods. Supervised Learning Methods: Prediction. Supervised Learning Methods: Classification. Advanced Analytics and Other SAS Data Mining Resources. Appendices. Index.