Modeling Techniques in Predictive Analytics with Python and R

Series
Pearson
Author
Thomas W. Miller  
Publisher
Pearson
Cover
Softcover
Edition
1
Language
English
Total pages
448
Pub.-date
October 2014
ISBN13
9780133892062
ISBN
0133892069
Related Titles


Product detail

Product Price CHF Available  
9780133892062
Modeling Techniques in Predictive Analytics with Python and R
88.90 approx. 7-9 days

Description

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).

Features

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

 

Author

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.