Graph Data Science: The Secret Ingredient for Relationship-Driven AI


You’ve probably heard that graph databases are a major trend in data science and analytics, and you may have wondered how to translate the buzz into business value.

In this session, you’ll learn about the fundamentals of graph data science: what graphs are, how they can be incorporated into your analytics practice, and how a connected data platform can help you move from proof of concept to production.

We will highlight real-world use cases from leading enterprise companies including fraud, recommendations, and supply chain optimization. You’ll discover how it’s possible to translate state of the science techniques into practical business value across multiple industries and use cases.


Phani Dathar is a Data Science Solution Architect at Neo4j. He is a computational scientist and holds a PhD in Nanotechnology and Computational Materials Science from Louisiana Tech University. After a decade of research in batteries and electrical energy storage in both industry and academia, he transitioned to a career in data science and machine learning and since worked with early-stage start-ups in AI/ML space and large organizations like American Airlines and Infosys as a data science consultant. He enjoys helping prospects and customers get started with Graph Data Science and was instrumental in launching Neo4j’s Graph Data Science training courses.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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