Web and Network Data Science: Modeling Techniques in Predictive Analytics

Web and Network Data Science: Modeling Techniques in Predictive Analytics

by Thomas W. Miller

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Product Details

ISBN-13: 9780133887648
Publisher: Pearson Education
Publication date: 12/19/2014
Series: FT Press Analytics
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 384
File size: 40 MB
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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


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