Graph Analytics: Modeling and Analyzing Network Data

Modern data is highly connected, yet traditional databases often struggle to capture the complex relationships between data points. Understanding how to model, store, and analyze these networks is essential for uncovering hidden patterns in social networks, recommendation engines, and fraud detection systems. This course guides you from the absolute basics of graph theory to designing and querying scalable graph databases. You will gain the skills needed to translate messy relational data into clean, connected graph models and perform powerful network analysis. What you'll learn: - Understand the foundational terminology of graph theory, including nodes, edges, properties, and paths. - Model complex real-world networks using property graphs and semantic web standards. - Write efficient queries to traverse graphs and retrieve connected data using standard query patterns. - Apply essential graph algorithms like PageRank, centrality, and community detection to find key network influencers. - Explore modern integrations of graph technology with vector databases and retrieval-augmented generation (RAG) systems. You will start with core definitions and structural concepts before progressing to practical data modeling, query design, and analytical algorithms. The written explanations and code examples ensure you can conceptualize and execute graph-based solutions immediately. This course is designed for beginners, data analysts, and software developers who want to expand their database toolkit, with no prior experience in graph databases required. Start reading today to unlock the power of highly connected data.