Module 1: Introduction to Graph Databases and Neo4j

• Graph theory fundamentals: nodes, relationships, and properties
• Graph database use cases: fraud detection, knowledge graphs, and recommendations
• Neo4j architecture: storage engine, query engine, and clustering
• Installing Neo4j Desktop and connecting to Neo4j Browser

Module 2: Cypher Query Language Fundamentals

• Cypher syntax: MATCH, WHERE, RETURN, and pattern matching
• Creating nodes and relationships: CREATE and MERGE statements
• Updating properties: SET and REMOVE operations
• Deleting nodes and relationships: DELETE and DETACH DELETE

Module 3: Advanced Cypher Queries

• OPTIONAL MATCH for left outer join behaviour
• WITH clause for query chaining and intermediate results
• UNWIND for list processing and result expansion
• Aggregation functions: count, sum, avg, min, max, and collect

Module 4: Graph Data Modelling

• Property graph model: nodes, relationships, labels, and properties
• Modelling best practices: when to use relationships vs properties
• Refactoring graph models: adding intermediate nodes and relationships
• Indexing: range indexes, full-text indexes, and point indexes

Module 5: Neo4j Graph Data Science Library

• Graph Data Science (GDS) plugin overview and projection
• Centrality algorithms: PageRank, Betweenness, and Degree Centrality
• Community detection: Louvain, Label Propagation, and WCC
• Similarity algorithms: Node Similarity and KNN

Module 6: Neo4j Performance and Optimisation

• Query profiling: EXPLAIN and PROFILE in Neo4j Browser
• Index usage and query plan optimisation
• Memory configuration: heap, page cache, and transaction memory
• Causal clustering: read replicas and core servers for scalability

Module 7: Neo4j Integrations and Applications

• Neo4j Python driver: connecting and running Cypher from Python
• Neo4j and Apache Spark: GraphX and Graph Data Science integration
• Importing data: LOAD CSV, neo4j-admin import, and APOC procedures
• Building a knowledge graph with NLP and entity extraction

Module 8: Capstone Project and Assessment

• Fraud detection graph model design and Cypher query development
• Graph Data Science analysis: community detection and PageRank
• Knowledge graph build with CSV import and Cypher queries
• Final assessment and course certification

Experience in the Industry Learn from Neo4j-certified graph database professionals who have built fraud detection systems, recommendation engines, knowledge graphs, and network analysis platforms using Neo4j for leading enterprises.

Backgrounds at the Top Our Neo4j trainers have worked at financial institutions, technology companies, and research organisations applying graph database technology to solve problems that relational databases cannot address efficiently.

Clear & Effective Teaching Neo4j architecture, Cypher query language, graph data modelling, Graph Data Science algorithms, performance optimisation, and integrations are explained with real graph analytics use cases and examples.

Hands-On Learning Focus Students build graph data models, write complex Cypher queries, run GDS algorithms for fraud and community detection, and build applications using the Neo4j Python driver through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Neo4j releases, Neo4j AuraDB cloud service, vector search capabilities, GenAI integrations, and evolving knowledge graph and graph analytics best practices.