Graph data science lets you leverage the power of relationships -- the connections between your data points -- to improve model prediction and answer previously intractable questions. Alicia Frame, Lead Product Manager for Data Science at Neo4j, will demonstrate the value of connected data for improving your data science and analytics applications. We'll walk through valuable use cases for graphs in data science, and then getting started using a knowledge graph, to using graph algorithms (like community detection or centrality) to answer global analytics questions, and finally improving machine learning pipelines using graph based feature engineering. Alicia will speak to how Neo4j's new Graph Data Science Library lets users get prototypes into production and show value quickly, empowering your team to incorporate cutting edge techniques into existing ML projects.
Following this, Matthew Sellwood, currently a Product Manager for IQVIA, will share an open source project that makes use of the graph data science library for lead optimisation of molecules in drug discovery. The project makes use of open source databases alongside the graph data science library to find new insights that could help chemists decide what molecule to make next in the process of designing a potential new drug.
Alicia Frame, Lead Product Manager for Data Science - Neo4j
Matthew Sellwood, Product Manager - IQVIA
Andy Steed, Content Director, Big Data LDN