WorldCat Linked Data Explorer

http://worldcat.org/entity/work/id/2281098179

Graph analysis and visualization discovering business opportunity in linked data

Wring more out of the data with a scientific approach toanalysis Graph Analysis and Visualization brings graph theory outof the lab and into the real world. Using sophisticated methods andtools that span analysis functions, this guide shows you how toexploit graph and network analytic techniques to enable thediscovery of new business insights and opportunities. Published infull color, the book describes the process of creating powerfulvisualizations using a rich and engaging set of examples fromsports, finance, marketing, security, social media, and more. Youwill find practical guidance towar.

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http://schema.org/description

  • "Wring more out of the data with a scientific approach toanalysis Graph Analysis and Visualization brings graph theory outof the lab and into the real world. Using sophisticated methods andtools that span analysis functions, this guide shows you how toexploit graph and network analytic techniques to enable thediscovery of new business insights and opportunities. Published infull color, the book describes the process of creating powerfulvisualizations using a rich and engaging set of examples fromsports, finance, marketing, security, social media, and more. Youwill find practical guidance towar."@en
  • "Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers.--Provided by publisher."@en
  • "This book brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. It describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including big data, plus clear instruction on the use of software and programming. Code examples using the popular Python programming language are provided. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, this is a thorough, authoritative resource. --"

http://schema.org/genre

  • "Electronic books"@en
  • "Electronic books"
  • "Livres électroniques"

http://schema.org/name

  • "Graph analysis and visualization discovering business opportunity in linked data"@en
  • "Graph analysis and visualization discovering business opportunity in linked data"
  • "Graph analysis and visualization : discovering business opportunity in linked data"
  • "Graph analysis and visualization : discovering business opportunity in linked data"@en
  • "Graph Analysis and Visualization Discovering Business Opportunity in Linked Data"
  • "Graph Analysis and Visualization Discovering Business Opportunity in Linked Data"@en