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Web and Network Data Science: Modeling Techniques in Predictive Analytics

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by Thomas W. Miller
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Web and Network Data Science: Modeling Techniques in Predictive Analytics by Thomas W. Miller

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.

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Ebook Details
Pages: 225
Size: 5.1 MB
Publisher: Pearson FT Press
Date published:   2014
ISBN: 9780133887648 (DRM-EPUB)
9780133887631 (DRM-PDF)

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This product is listed in the following categories:

Nonfiction > Business & Economics > Production & Operations Management
Nonfiction > Computers > Database Management > Database Mining
Nonfiction > Technology > Operations Research

This author has products in the following categories:

Nonfiction > Business & Economics > Statistics
Nonfiction > Business & Economics > Production & Operations Management
Nonfiction > Computers > Database Management > Database Mining
Nonfiction > Technology > Operations Research
Nonfiction > Business & Economics > Marketing

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03/25/2017
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