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Web and Network Data Science: Modeling Techniques in Predictive Analytics

Web and Network Data Science: Modeling Techniques in Predictive Analytics

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  • Copyright 2015
  • Dimensions: 7" x 9-1/4"
  • Pages: 384
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-388767-7
  • ISBN-13: 978-0-13-388767-9

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.

Sample Content

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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