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

Thomas W. Miller  
Total pages
October 2014
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Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science
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Today, successful firms win by understanding their data more deeply than competitors do. They compete based on analytics. In Modeling Techniques in Predictive Analytics, the Python edition, the leader of Northwestern University’s prestigious analytics program brings together all the up-to-date concepts, techniques, and Python code you need to excel in analytics.


Thomas W. Miller’s balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. This important reference addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, Web and text analytics, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains:

  • Why the problem is significant
  • What data is relevant
  • How to explore your data
  • How to model your data – first conceptually, with words and figures; and then with mathematics and programs

Miller walks through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. Extensive example code is presented in Python, a new and extremely popular language for applied statistics, statistical research, and predictive modeling; all code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).


Today's definitive, comprehensive guide to using predictive analytics to overcome business challenges – now updated and reorganized for more effective learning!

  • Teaches modeling techniques conceptually, with words and figures – and then mathematically, with the powerful Python language
  • Restructured standalone chapters provide fast access to all the knowledge you need to solve any category of problem
  • Covers segmentation, brand positioning, product choice modeling, pricing, finance, sports analytics, Web/text analytics, social network analysis, and more
  • Helps you leverage traditional techniques, machine learning, data visualization, and statistical graphics
  • Designed for wide applicability and ease of use: requires no linear algebra or advanced math
  • Contains updated source material throughout
  • Now leads directly into Pearson's pioneering Data Science Series: cutting-edge texts on advanced modeling for business managers, modelers, and programmers alike

Table of Contents

Preface     v

1  Analytics and Data Science     1

2  Advertising and Promotion     16

3  Preference and Choice     33

4  Market Basket Analysis     43

5  Economic Data Analysis     61

6  Operations Management     81

7  Text Analytics     103

8  Sentiment Analysis 1    35

9  Sports Analytics     187

10  Spatial Data Analysis     211

11  Brand and Price     239

12  The Big Little Data Game     273

A  Data Science Methods     277

  A.1 Databases and Data Preparation     279

  A.2 Classical and Bayesian Statistics     281

  A.3 Regression and Classification     284

  A.4 Machine Learning     289

  A.5 Web and Social Network Analysis     291

  A.6 Recommender Systems     293

  A.7 Product Positioning     295

  A.8 Market Segmentation     297

  A.9 Site Selection     299

  A.10 Financial Data Science     300

B  Measurement     301

C  Case Studies     315

  C.1 Return of the Bobbleheads     315

  C.2 DriveTime Sedans     316

  C.3 Two Month’s Salary     321

  C.4 Wisconsin Dells     325

  C.5 Computer Choice Study     330

D  Code and Utilities     335

Bibliography     379

Index     413



THOMAS W. MILLER is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, and the capstone course. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science.


Miller is co-founder and director of product development at ToutBay, a publisher and distributor of data science applications. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets, and has worked with predictive models for over 30 years. Miller’s books include Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.


Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin–Madison.


He holds a Ph.D. in psychology (psychometrics) and a master’s degree in statistics from the University of Minnesota, and an MBA and master’s degree in economics from the University of Oregon.