The relationships in your data tell stories that row-and-column tables can't surface. Graph analytics gives you a way to map those connections at scale, turning networks of entities, transactions, and interactions into structures that reveal patterns no traditional query can show. Once you can read those patterns, you'll answer questions that table-based tools simply can't frame.

In this course, you'll trace how graph analytics works from first principles, comparing it with traditional relational databases to see exactly where and why it outperforms them. You'll apply the core framework of nodes, edges, and properties to real-world problems in logistics, social media, and financial fraud detection, and then survey the providers and query languages that power graph databases in production today. By the end, you'll be able to identify which data problems call for a graph approach, map any network scenario to the correct nodes, edges, and properties, and choose among the leading graph database providers with a clear, defensible rationale.e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.