Overview

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.

Syllabus

Introduction to Neo4j

In this module, we will introduce you to Neo4j and its key concepts, including graph databases and the property graph model. You’ll explore real-world use cases and learn how to set up Neo4j, configure environments, and use the Neo4j Browser to explore graph data.

Cypher Query Language

In this module, we will dive into Cypher, the query language for Neo4j. You’ll learn to construct Cypher queries, explore filtering, aggregation, and advanced clauses, and apply hands-on labs to reinforce your learning through practical exercises.

Use Case – Flights Data: Graph Data Science Library and Its Usage

In this module, we will explore the power of the Neo4j Graph Data Science library with real-world use cases. Through hands-on labs, you'll practice implementing graph algorithms to analyze flight data, uncover insights, and optimize graph-based decision-making.

Use Case – Crime Investigation: Advanced Cypher – UNWIND, COLLECT

In this module, we will focus on using advanced Cypher techniques to solve practical problems, including crime investigation scenarios. You'll gain experience with powerful query techniques to analyze and solve complex data challenges in a hands-on lab setting.

Basic Overview of GraphQL

In this module, we will introduce you to GraphQL, a query language for APIs, and its integration with Neo4j. You’ll set up GraphQL tools, define schemas, and practice writing queries and mutations through interactive labs.

Miscellaneous Topics

In this module, we will cover a variety of miscellaneous but essential topics, including plugin installations, data import techniques, and visualization with Neo4j Bloom. You’ll also explore when Neo4j may not be the best solution for certain use cases.

Performance Optimization

In this module, we will focus on optimizing performance in Neo4j. You will learn key techniques such as memory allocation strategies, query profiling, and indexing to boost the efficiency of your graph database operations.

Interacting with Neo4j from a Python Program

In this module, we will guide you through setting up a Python environment to interact with Neo4j. You’ll learn to write Python code that integrates seamlessly with Neo4j to create nodes, relationships, and automate graph operations.

Emerging Trends in Neo4j and AI Integration: LLMs and GraphRAG (Advanced Topic)

In this module, we will explore the intersection of Neo4j and advanced AI techniques like large language models (LLMs) and Retrieval-Augmented Generation (RAG). You’ll learn how to build knowledge graphs from unstructured data and use them to enhance AI capabilities in real-world applications.