Description
With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available. You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn. Victor Lee, Xinyu Chan, and Gaurav Deshpande from TigerGraph present real use cases covering several contemporary business needs. By diving into hands-on exercises using TigerGraph Cloud, you'll quickly become proficient at designing and managing advanced analytics and machine learning solutions for your organization. Use graph thinking to connect, analyze, and learn from data for advanced analytics and machine learning Learn how graph analytics and machine learning can deliver key business insights and outcomes Use five core categories of graph algorithms to drive advanced analytics and machine learning Deliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizen Discover insights from connected data through machine learning and advanced analytics
About the Author
Victor Lee is Vice President of Machine Learning and AI at TigerGraph. His Ph.D. dissertation was on graph-based similarity and ranking. Dr. Lee has co-authored book chapters on decision trees and dense subgraph discovery. Teaching and training have also been central to his career journey, with activities ranging from developing training materials for chip design to writing the first version of TigerGraph's technical documentation, from teaching 12 years as a full-time or part-time classroom instructor to presenting numerous webinars and in-person workshops. Phuc Kien Nguyen is a data scientist at ABN Amro Bank in Amsterdam. For the past five years, he has helped develop solutions and machine learning models to combat financial crime. He holds an MSc degree in Information Architecture from Delft University of Technology. Next to his day-to-day job, he writes articles at Medium about data science and network analytics. He has a great passion for storytelling, especially through video games. In his spare time, he loves to play football and catch up with the latest development in technology.
Book Information
ISBN 9781098106652
Author Victor Lee, Ph.D.
Format Paperback
Page Count 300
Imprint O'Reilly Media
Publisher O'Reilly Media
About the Author
Victor Lee is Vice President of Machine Learning and AI at TigerGraph. His Ph.D. dissertation was on graph-based similarity and ranking. Dr. Lee has co-authored book chapters on decision trees and dense subgraph discovery. Teaching and training have also been central to his career journey, with activities ranging from developing training materials for chip design to writing the first version of TigerGraph's technical documentation, from teaching 12 years as a full-time or part-time classroom instructor to presenting numerous webinars and in-person workshops. Phuc Kien Nguyen is a data scientist at ABN Amro Bank in Amsterdam. For the past five years, he has helped develop solutions and machine learning models to combat financial crime. He holds an MSc degree in Information Architecture from Delft University of Technology. Next to his day-to-day job, he writes articles at Medium about data science and network analytics. He has a great passion for storytelling, especially through video games. In his spare time, he loves to play football and catch up with the latest development in technology.
Book Information
ISBN 9781098106652
Author Victor Lee, Ph.D.
Format Paperback
Page Count 300
Imprint O'Reilly Media
Publisher O'Reilly Media
