Abstract: 

Graphs are hot in data science right now, but what does that even mean? Graph data science encompasses everything from classical network science to cutting edge neural networks, so it can be hard to understand how graphs can be useful for your project, and where you can get started. This talk will cover the basics - how graphs are relevant to the problems you need to solve - and how to get started using graph techniques. You'll learn how to improve your predictions with the data you already have, and how to use graph algorithms and machine learning to find what's most important in your connected data.

Bio: 

Alicia Frame is the lead product manager for data science at Neo4j. She's spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j's Graph Data Science Library. She has a Ph.D. in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences.

She's worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments -- and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.