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  • 1
    Online Resource
    Online Resource
    Cary, N.C. : SAS Institute | Boston, Mass. :Safari Books Online,
    ISBN: 9781599940472 , 1599940477
    Language: English
    Pages: xxii, 408 p , ill. , 28 cm
    Series Statement: SAS Press series
    Keywords: SAS (Computer file) ; Enterprise Miner ; Business ; Data processing ; Electronic data processing ; Commercial analysis ; Data marts ; Data mining ; Time-series analysis ; Electronic books ; local
    Abstract: 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!
    Note: Includes index
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Cary, N.C. : SAS Institute
    ISBN: 9781612902272 , 1612902278
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Parallel Title: Erscheint auch als
    Keywords: SAS (Computer file) ; Business intelligence ; Business ; Data processing ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.
    Note: Includes bibliographical references and index. - Description based on print version record
    Library Location Call Number Volume/Issue/Year Availability
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