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Full Description
To succeed with predictive analytics, you must understand it on three levels:Strategy and managementMethods and modelsTechnology and codeThis up-to-the-minute reference thoroughly covers all three categories.Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you're new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you're already a modeler, programmer, or manager, it will teach you crucial skills you don't yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations-not complex math. Thomas W. Miller, leader of Northwestern University's pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more.Every chapter focuses on one of today's key applications for predictive analytics, delivering skills and knowledge to put models to work-and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/millerIf you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You'll learn why each problem matters, what data are relevant, and how to explore the data you've identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You'll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods.Gain powerful, actionable, profitable insights about:Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Contents
Preface v1 Analytics and Data Science 12 Advertising and Promotion 143 Preference and Choice 294 Market Basket Analysis 375 Economic Data Analysis 536 Operations Management 677 Text Analytics 838 Sentiment Analysis 1079 Sports Analytics 14310 Spatial Data Analysis 16711 Brand and Price 18712 The Big Little Data Game 221A Data Science Methods 225A.1 Databases and Data Preparation 227A.2 Classical and Bayesian Statistics 229A.3 Regression and Classification 232A.4 Machine Learning 237A.5 Web and Social Network Analysis 239A.6 Recommender Systems 241A.7 Product Positioning 243A.8 Market Segmentation 245A.9 Site Selection 247A.10 Financial Data Science 248B Measurement 249C Case Studies 263C.1 Return of the Bobbleheads 263C.2 DriveTime Sedans 264C.3 Two Month's Salary 269C.4 Wisconsin Dells 273C.5 Computer Choice Study 278D Code and Utilities 283Bibliography 321Index 355
