Web And Network Data Science
Modeling Techniques In Predictive Analytics
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
|Titel:||Web And Network Data Science|
|auteur:||Miller, Thomas W.|
|Uitgever:||Pearson Education (us)|
|Plaats van publicatie:||01|
|Afmetingen:||240 x 186 x 27|
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.
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