Bite-Sized Neo4j for Data Scientists
Dive into a comprehensive tutorial series designed for data scientists to master Neo4j, a popular graph database management system. Learn to connect Jupyter to a Neo4j Sandbox, utilize Python drivers like py2neo and Neo4j Python Driver, and execute basic Cypher queries. Explore techniques for populating the database from various sources including Pandas, CSV files, JSON files, and the neo4j-admin tool. Advance your skills with in-memory graph creation, RDF data import from Wikidata, and implementation of graph algorithms for centrality calculation and community detection. Discover how to create FastRP graph embeddings, visualize graph data using Neo4j Bloom, and integrate graph embeddings into machine learning models. Compare SQL and graph database approaches, highlighting scenarios where graph querying proves more efficient. Perfect your graph database skills through hands-on examples and practical applications in data science workflows.