Modeling Techniques in Predictive Analytics with Python and R : a Guide to Data Science.

Includes bibliographical references (pages 379-412) and index.

Analytics and data science -- Advertising and promotion -- Preference and choice -- Market basket analysis -- Economic data analysis -- Operations management -- Text analytics -- Sentiment analysis -- Sports analytics -- Spatial data analysis -- Brand and price -- The big little data game -- [Appendix] A. Data science methods -- [Appendix] B. Measurement -- [Appendix] C. Case studies -- [Appendix] D. Code and utilities.

Thomas W. Miller's balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you're new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you're already a modeler, programmer, or manager, you'll learn crucial skills you don't already have. Using Python and R, Miller addresses multiple business 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 code that delivers actionable insights. You'll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods.