[原创][下载]Data Preparation for Analytics using SAS (Ebook+Code+Slide+Paper)

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本帖最后由 sascylindrical 于 2009-7-9 00:46 编辑

【书名】Data Preparation for Analytics using SAS
【作者】Gerhard Svolba
【出版社】SAS Publishing
【版本】First edition
【语言】英语
【出版日期】January 19, 2007
【文件格式】PDF (清晰电子书)
【文件大小】33M （解压后）
【页数】440 pages
【ISBN出版号】1599940477
【资料类别】SAS实用工具书
【市面定价】$67.95
【扫描版还是影印版】清晰电子书
【是否缺页】否
【关键词】 Enterprise Miner, transactional data, customer data mart, customer segmentation measures
【Amazon链接】
http://www.amazon.com/Data-Preparation-Analytics-Using-Press/dp/1599940477
【目录】
Part 1 Data Preparation: Business Point of View
Chapter 1 Analytic Business Questions
Chapter 2 Characteristics of Analytic Business Questions
Chapter 3 Characteristics of Data Sources
Chapter 4 Different Points of View on Analytic Data Preparation
Part 2 Data Structures and Data Modeling
Chapter 5 The Origin of Data
Chapter 6 Data Models
Chapter 7 Analysis Subjects and Multiple Observations
Chapter 8 The One Row-per-Subject Data Mart
Chapter 9 The Multiple-Rows-per-Subject Data Mart
Chapter 10 Data Structures for Longitudinal Analysis
Chapter 11 Considerations for Data Marts
Chapter 12 Considerations for Predictive Modeling
Part 3 Data Mart Coding and Content
Chapter 13 Accessing Data
Chapter 14 Transposing One- and Multiple-Rows-per-Subject Data Structures
Chapter 15 Transposing Longitudinal Data
Chapter 16 Transformations of Interval-Scaled Variables
Chapter 17 Transformations of Categorical Variables
Chapter 18 Multiple Interval-Scaled Observations per Subject
Chapter 19 Multiple Categorical Observations per Subject
Chapter 20 Coding for Predictive Modeling
Chapter 21 Data Preparation for Multiple-Rows-per-Subject and Longitudinal Data Marts
Part 4 Sampling, Scoring, and Automation
Chapter 22 Sampling
Chapter 23 Scoring and Automation
Chapter 24 Do’s and Don’ts When Building Data Marts
Part 5 Case Studies
Chapter 25 Case Study 1—Building a Customer Data Mart 305
Chapter 26 Case Study 2—Deriving Customer Segmentation Measures from Transactional Data
Chapter 27 Case Study 3—Preparing Data for Time Series Analysis
Chapter 28 Case Study 4—Data Preparation in SAS Enterprise Miner
【Editorial Reviews】
Review
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
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
Product Description
Written for anyone involved in the data preparation process for analytics, this user-friendly text offers practical advice in the form of SAS coding tips and tricks, along with providing the reader with a conceptual background on data structures and considerations from the business point of view. Topics addressed include viewing analytic data preparation in the light of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations for 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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