What is included with this book?
To succeed with predictive analytics, you must understand it on three levels:
Strategy and management
Methods and models
Technology and code
This 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/miller
If 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:
THOMAS W. MILLER (Evanston, IL), faculty director of Northwestern University’s Predictive Analytics program, has designed and taught courses in predictive analytics, predictive modeling, marketing analytics, and advanced modeling. Also owner of Research Publishers LLC, he has worked with predictive models for 30+ years, and consults on retail site selection, product positioning, segmentation, and pricing. He holds a Ph.D. in psychology (psychometrics); and M.S. degrees in statistics, business, and economics. His books include Data and Text Mining: A Business Applications Approach; Research and Information Services: An Integrated Approach for Business, and Without a Tout: How to Pick a Winning Team. He previously directed the A.C. Nielsen Center for Marketing Research in the School of Business, U. of Wisconsin-Madison.
2. Brand Equity Analysis
3. Competitive Analysis
4. Customer Satisfaction, Loyalty, and Churn
5. Financial Market Analysis
6. Investment Analysis
7. Market Segmentation
8. New Product Research
9. Pricing Research
10. Product Design
11. Product Positioning
12. Recommender Systems
13. Risk Analytics
14. Sales Forecasting
15. Sales Promotion
16. Sentiment Analysis
17. Site Selection
18. Social Network Analysis
19. Target Marketing
20. Transportation PlanningToday's most accessible guide to predictive analytics for managers, analysts, and programmers -- now updated and restructured for more effective learning!