Web and Network Data Science: Modeling Techniques in Predictive Analytics / Edition 1

by Thomas W. Miller

Overview

Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics.

Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications.

Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Product Details

ISBN-13:	9780133886443
Publisher:	Pearson FT Press
Publication date:	01/04/2015
Series:	FT Press Analytics Series
Pages:	384
Product dimensions:	6.90(w) x 9.40(h) x 1.10(d)

About the 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 Modeling Techniques in Predictive Analytics (Revised and Expanded Edition), Modeling Techniques in Predictive Analytics with Python and R, 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.

Table of Contents

Preface v

1 Being Technically Inclined 1

2 Delivering a Message Online 13

3 Crawling and Scraping the Web 25

4 Testing Links, Look, and Feel 43

5 Watching Competitors 55

6 Visualizing Networks 69

7 Understanding Communities 95

8 Measuring Sentiment 119

9 Discovering Common Themes 171

10 Making Recommendations 201

11 Playing Network Games 223

12 What’s Next for the Web? 233

A Data Science Methods 237

A.1 Databases and Data Preparation 240

A.2 Classical and Bayesian Statistics 242

A.3 Regression and Classification 245

A.4 Machine Learning 250

A.5 Data Visualization 252

A.6 Text Analytics 253

B Primary Research Online 261

C Case Studies 281

C.1 Email or Spam? 281

C.2 ToutBay Begins 284

C.3 Keyword Games: Dodgers and Angels 288

C.4 Enron Email Corpus and Network 291

C.5 Wikipedia Votes 292

C.6 Quake Talk 294

C.7 POTUS Speeches 295

C.8 Anonymous Microsoft Web Data 296

D Code and Utilities 297

E Glossary 313

Bibliography 321

Index 351