9780133892062

Modeling Techniques in Predictive Analytics with Python and R A Guide to Data Science

by
  • ISBN13:

    9780133892062

  • ISBN10:

    0133892069

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 10/1/2014
  • Publisher: Ft Pr
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Supplemental Materials

What is included with this book?

  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
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Summary

Master predictive analytics, from start to finish

 

Start with strategy and management

Master methods and build models

Transform your models into highly-effective code—in both Python and R

 

This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math.

 

Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each 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.

 

Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code.

 

If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more.

 

All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/

 

Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage.

 

Thomas W. Miller’s unique 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.

 

Use Python and R to 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

 

Author Biography

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.

Table of Contents

1. Introduction

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 Planning

21. Work Force Scheduling

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