Neo4j Graph Database Workshop for the Data Scientist Using Python
Graph databases provide a flexible and intuitive data model that is ideal for many data science use cases such as ad-hoc data analysis, generating personalized recommendations, social network analysis, natural language processing, and fraud detection. In addition Cypher, the query language for graphs, allows for traversing the graph by defining expressive graph queries using graph pattern matching. In this workshop we will work through a series of hands on use cases using Neo4j and common Python science tools such as pandas, igraph, and matplotlib. We will cover how to connect to Neo4j from Python, an overview of how to query graphs using Cypher, how to import data into Neo4j, data visualization, and how to use Python data science tools in conjunction with Neo4j for network analysis, generating recommendations, and fraud detection. Attendees should install Neo4j, Jupyter and be somewhat familiar with Python to get the most out of the session.
William Lyon is a software developer at Neo4j, the open source graph database. As an engineer on the Developer Relations team, he works primarily on integrating Neo4j with other technologies, building demo apps, helping other developers build applications with Neo4j, and writing documentation. Prior to joining Neo, William worked as a software developer for several startups in the real estate software, quantitative finance, and predictive API fields. William holds a Masters degree in Computer Science from the University of Montana. You can find him online at lyonwj.com
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