Graph-Powered Analytics and Machine Learning with TigerGraph: Driving Business Outcomes with Connected Data

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.