Neo4j: Cypher, GDS, GraphQL, LLM, Knowledge Graphs for RAG

About this course

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Neo4j is a powerful graph database widely used for managing and analyzing complex relationships in data. In this course, you’ll dive deep into Neo4j’s features, from Cypher query language to advanced Graph Data Science (GDS) algorithms, and learn how to integrate Neo4j with modern technologies like GraphQL, Large Language Models (LLMs), and knowledge graphs for retrieval-augmented generation (RAG). By exploring hands-on labs, you’ll master creating, querying, and analyzing graph data in real-world scenarios. The course starts by introducing the foundational concepts of Neo4j, including its graph model and database setup. You will learn how to work with the Cypher query language to perform CRUD operations, pathfinding, and aggregation. Moving forward, you’ll apply these techniques in real-world use cases like analyzing flight data and investigating crimes. As you progress, you’ll also explore the Graph Data Science library to run algorithms like centrality and community detection. You’ll even learn how to work with GraphQL to query and mutate data, integrating Neo4j with external applications. The course is suitable for anyone interested in data science, machine learning, and graph database applications. A basic understanding of databases and programming concepts will be helpful, but no prior experience with Neo4j is required. The difficulty level is intermediate, designed to provide practical skills through hands-on labs and real-world problem solving. By the end of the course, you will be able to efficiently query and analyze graph data, implement data science algorithms with Neo4j, build knowledge graphs using LLMs, and integrate Neo4j with other technologies for advanced AI applications like RAG.