Overview

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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.

Syllabus

Reading Graph Databases with Nodes, Edges, and Properties

You already work with data in tables and rows, now it's time to see what tables can't show you. In this module, you'll discover how graph analytics maps relationships between data points using nodes, edges, and properties, and why this approach surfaces insights that traditional relational databases can't reach.

Applying Graph Analytics Across Real-World Scenarios

You're working with data that has a story hidden in its connections — now it's time to read that story. In this module, you'll trace how graph analytics is applied across logistics, social media, and financial systems, and work through two concrete use cases: identifying the most influential nodes in a social network for marketing purposes, and detecting money laundering patterns in financial transaction data.

Navigating the Graph Analytics Tools and Providers Landscape

The graph analytics ecosystem has its own providers, databases, and query languages — and navigating it confidently is what separates a practitioner who can implement from one who can only describe. In this module, you'll survey the leading graph database providers and query languages in use today, so you can make informed decisions about which tools fit which problems.

Conclusion: Next Steps in Graph Analytics

You've built a working foundation in graph analytics, now it's time to put it to work. In this module, you'll take stock of what you can now do, identify the sandboxes and resources that will let you keep building hands-on experience, and connect with the community of practitioners working in this field.