Neo4j Graph Data Science and Data Ingestion Essentials

Working with deeply connected data requires tools designed specifically to uncover relationships and patterns that traditional databases miss. This written course provides a clear pathway to modeling, importing, and analyzing graph data using Neo4j and its Graph Data Science library. You will move from fundamental graph concepts to practical data import strategies and core analytical techniques, learning how to query and extract value from networked information through structured reading and practical text-based exercises. What you'll learn: - Understand core graph database concepts, terminology, nodes, relationships, and property graphs - Design efficient graph data models tailored for analytical queries - Master data ingestion techniques to import CSV and structured data using Cypher - Configure and manage in-memory graph projections for fast computation - Apply essential Graph Data Science algorithms for centrality, community detection, and similarity - Understand graph embeddings and modern patterns for connecting graph structures to data analysis workflows The course begins with foundational graph theory and terminology before guiding you through data ingestion workflows, Cypher import commands, and hands-on Graph Data Science algorithm execution. This course is designed for beginners, data analysts, and software developers new to graph databases, with no prior Neo4j experience required. Start reading today to unlock the analytical power of graph data.