Abstract: Data scientists are using graphs to help improve predictions and enhance their machine learning models. Application developers are extending their use of Neo4j’s native graph platform to incorporate native graph algorithms to build more intelligent applications. Using the two together shows us how connected data can impact our understanding of data structure and improve our analysis and predictions. Graph data analytics provides capabilities and insight that other tools and stores cannot uncover.
In this session, you will learn how to combine Neo4j, the Cypher query language, and graph algorithms for data science and analytics uses. The presenters will discuss the Neo4j graph database through an analytics lens and explain how Neo4j's graph algorithms library is architected. We’ll cover exploratory analysis as well as how to extract more predictive patterns and structures in your data using graph algorithms. Through a deep-dive of many of the algorithms, you will understand how and when to use them. This is where the fun and real-life use begins, as we step through several hands-on tutorials with Neo4j, Cypher, and the algorithms, as well as introducing a helpful UI tool (NEuler). Come join us to gain the knowledge of how and when to apply Neo4j and graph algorithms to your own data sets and to gather resources and hands-on experience in using them for data science! NOTE: All hands-on guides and sample code and data will be available throughout the training, as well as accessible 24/7 after the session for further exploration and analysis.
Bio: Coming Soon!
Developer Relations Engineer | Neo4j
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