Course Outline

🔹 Day 1: Introduction to Graph Databases and Neo4j

Session 1: Fundamentals of Graph Databases

• What is a graph database?
• Graph data models: nodes, relationships, properties
• Comparison with relational databases and use cases

Session 2: Getting Started with Neo4j

• Neo4j architecture and setup
• Creating nodes and relationships using Neo4j Browser and Cypher
• Importing data and basic CRUD operations

Session 3: Writing Queries with Cypher

• MATCH, WHERE, RETURN clauses
• Filtering and pattern matching
• Creating, updating, and deleting graph data

Lab Activities:

• Install Neo4j and create a sample graph dataset
• Write basic Cypher queries to explore the graph
• Perform CRUD operations on nodes and relationships

🔹 Day 2: Advanced Cypher Queries and Graph Modeling

Session 1: Complex Pattern Matching and Query Optimization

• Variable length path queries
• Using WITH and aggregations
• Optimizing Cypher queries and profiling

Session 2: Modeling Complex Relationships

• Designing graph schemas for real-world problems
• Handling hierarchical and many-to-many relationships
• Working with labels, indexes, and constraints

Session 3: Graph Algorithms Overview

• Introduction to common graph algorithms (shortest path, PageRank, community detection)
• Using the Neo4j Graph Data Science Library
• Querying algorithm results and visualization

Lab Activities:

• Write advanced Cypher queries with path patterns and aggregations
• Model a social network or recommendation system graph
• Run graph algorithms and interpret results

🔹 Day 3: Integrations, Scaling, and Real-World Applications

Session 1: Integrating Neo4j with Applications

• Neo4j drivers and APIs (JavaScript, Python, Java)
• Building RESTful APIs with Neo4j
• Using Neo4j Aura (cloud service) and Neo4j Desktop

Session 2: Scaling and Security

• Neo4j clustering and high availability
• Backup and restore procedures
• Security best practices and role-based access

Session 3: Case Studies and Project Work

• Use cases: fraud detection, recommendation engines, network analysis
• Building a mini project: e.g., movie recommendation or social graph analysis
• Performance tuning and troubleshooting

Lab Activities:

• Connect Neo4j to a sample web app or script
• Configure security settings and create roles
• Complete a mini project leveraging graph data and algorithms