Graph Databases with Neo4j
Neo4j is the leading graph database, built for data where relationships are as important as the data itself — social networks, fraud detection, recommendation engines, and knowledge graphs all fit this model naturally. Unlike relational databases, Neo4j stores connections as first-class citizens, making traversals across deeply linked data fast and expressive. This track covers the full stack: from graph theory and the Cypher query language through data modeling, application integration, security, and production-scale performance tuning.
What You Will Learn
You will write Cypher queries from simple pattern matches to advanced aggregations and path-finding operations. You will apply graph algorithms for centrality, community detection, and shortest-path analysis. You will design graph data models that reflect real-world domain relationships, integrate Neo4j into applications, configure security and access control, and use the Graph Data Science library for analytical workloads. Later courses cover administration, performance tuning, scaling strategies, and extending Neo4j with custom procedures and plugins.
The Learning Path
Twelve courses span A1 through C1. The track opens with Introduction to Graph Databases and Neo4j and Cypher Query Language Fundamentals, then moves through Advanced Cypher Querying Techniques and Graph Data Modeling Best Practices at the B level. The final five courses are all C1: Neo4j Administration and Data Management, Advanced Graph Data Science with GDS, Performance Tuning and Scaling Neo4j, Real-World Neo4j Use Cases and Patterns, and Extending Neo4j Capabilities.
How It Works
Each course is split into short, focused lessons you complete in the built-in editor with real-time feedback and an AI tutor available whenever you get stuck. You write Cypher against live graph examples from the first lesson, building muscle memory alongside conceptual understanding.
