Abstract: 

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.

Bio: 

Katie is a Data Science Solution Architect at Neo4j. She completed her degree in Cognitive Neuroscience at Harvard University. Passionate about people and problem solving, she transitioned to focusing on helping people and businesses leverage data for impactful outcomes. As a customer-facing data scientist she has had the opportunity to work with large and small organizations across a variety of industries. At Neo4j she helps teams up-level their data science practice with graph data science.