Written for anyone involved in the data preparation process for analytics, Gerhard Svolba's Data Preparation for Analytics Using SAS offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view. The tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more!
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Dr. Gerhard Svolba is a senior solutions architect and analytic expert at SAS Institute Inc. in Austria, where he specializes in analytics in different business and research domains. His project experience ranges from business and technical conceptual considerations to data preparation and analytic modeling across industries. He is the author of Data Preparation for Analytics Using SAS and teaches a SAS training course called "Building Analytic Data Marts."
This book was designed with businesses in mind, but the basic ideas apply easily to all sorts of research endeavors in which decision makers must gather and use data that were initially collected for some other purpose. In my opinion, the book has two great strengths. First, the technical material in the book is wrapped in a sense of purpose and an awareness of the importance of context. The second great strength is the book's organization and clarity. . . . The development of ideas and examples is clear and orderly, exactly as it should be in a work of this type. --Michael T. Brannick PhD, Professor Graduate Program Director, Psychology Department, University of South Florida
Dr. Svolba does an excellent job of illustrating analytic examples in a business environment. He does not constrain himself to 'correct' statistical analysis. Instead, he puts a strong emphasis on matching the business goal with the actual data preparation. Most of the examples in this book are designed with a specific business goal in mind. Therefore, this book is also very useful for people with little business background, such as recent graduates, to add the business perspective into their data mining exercise. --Jin Li, Statistician, Capital One, Financial Services
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