Web and Network Data Science: Modeling Techniques in Predictive Analytics - Hardcover

Miller, Thomas W.

9780133886443: Web and Network Data Science: Modeling Techniques in Predictive Analytics

Hardcover

ISBN 10: 0133886441 ISBN 13: 9780133886443

Publisher: Pearson FT Press, 2014

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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From the Back Cover:

TO SOLVE REAL PROBLEMS, YOU NEED TO MASTER BOTH SIDES OF PREDICTIVE ANALYTICS MODELING:

BUSINESS APPLICATIONS AND CORE PRINCIPLES NOW, ONE AUTHORITATIVE GUIDE COVERS THEM BOTH

In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious Predictive Analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling.

Some books in this field focus either entirely on business issues such as website performance (Google Analytics), search engine optimization (SEO), or web competitive intelligence. Others are strictly academic, covering concepts from economics, sociology, or network science. This text gives 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 website performance, usage analysis, social media platforms, SEO, automated data acquisition from the web, and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both data science and network science, showing how to use their powerful tools to solve real business problems.

If you want competitive advantage, you need knowledge. If you want knowledge, start with the web—the largest data repository ever created. But knowledge and understanding do not come from data alone. To gain those, you must apply the cutting-edge techniques of web and network data science.

This book will show you how. This is the first text to integrate academic principles and concepts with real-world applications, offering realistic examples built with the world’s leading tools: Python for data preparation and R for modeling and visualization.

Based on his pioneering course at Northwestern University, Thomas Miller covers topics ranging from website usability and performance testing to advanced social network analysis for identifying leaders and influencers.

Using real datasets, Miller demonstrates powerful ways to predict individual or group behavior in purchasing and voting; glean high-value competitive intelligence; and answer a wide spectrum of general and domain-specific questions.

Researchers and analysts can use Web and Network Data Science as a ready resource and reference for online research and modeling projects. For programmers, there is a complete foundation of working code for solving real problems—with step-by-step comments and expert guidance for taking your analysis even further.

USE WEB AND NETWORK MODELING TO:

Evaluate website performance
Gather data in an automated fashion
Learn more about competitors
Visualize complex networks
Understand communities and their hidden dynamics
Measure sentiment about products or issues
Discover common themes in politics and beyond
Make high-value business recommendations
Simulate complex real-world phenomena
...And much more...

ALL DATA SETS, EXTENSIVE PYTHON AND R CODE, AND ADDITIONAL EXAMPLES available for download at http://www.ftpress.com/miller

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.

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