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Web And Network Data Science

Modeling Techniques In Predictive Analytics

Web And Network Data Science - Miller, Thomas W. - ISBN: 9780133886443
Prijs: € 81,95
Levertijd: 5 tot 15 werkdagen
Bindwijze: Boek, Gebonden
Genre: Databases
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Beschrijving

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

Details

Titel: Web And Network Data Science
auteur: Miller, Thomas W.
Mediatype: Boek
Bindwijze: Gebonden
Taal: Engels
Druk: 1
Aantal pagina's: 384
Uitgever: Pearson Education (us)
Plaats van publicatie: 01
NUR: Databases
Afmetingen: 240 x 186 x 27
Gewicht: 824 gr
ISBN/ISBN13: 9780133886443
Intern nummer: 26573035

Biografie (woord)

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.

 

Inhoudsopgave

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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