Graph Data Science with Python and Neo4j is your ultimate guide to unleashing the potential of graph Data Science by blending Python's robust capabilities with Neo4j's innovative graph database technology. From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical application, providing you with the hands-on skills needed to tackle real-world challenges. Python and Neo4j are both terrific tools for graph data science. Each offers powerful tools and frameworks to analyze interconnected data. In theory, it is possible to implement end-to-end graph data science with either Python or Neo4j individually. However, in practice, it is much simpler to leverage both tools together to maximize the value of each tool. Python has become one of the most popular programming languages in the data science community due to its rich ecosystem of libraries and packages, allowing all users to leverage the incredible work of others and jumpstart their analysis. Python comes with several integrated development environments (IDEs) that facilitate code visualization, allowing users not only to view and debug their code but also to create graphs, charts, and various other visual representations of data.