Once we have the basics, we will show you how to design and implement a graph data model and associated queries. With a mixture of instruction and hands-on practice sessions, you’ll learn how to apply the property graph model to solve common modeling problems. You’ll also learn how to evolve an existing graph in a controlled manner to support new or changed requirements.

The Neo4j Training covers:

• Key use cases
• Neo4j features
• Cypher, Neo4j’s Graph Query Language
• An understanding of the labeled property graph
• How to apply the property graph to common modeling problems
• Common graph structures for modeling complex, connected scenarios
• Criteria for choosing between different modeling options
• How to modify an existing model to accommodate new requirements

Using a movie recommendation dataset we will learn how to model and import the data.

We'll explore the imported data visually and then start our first steps with the query language Cypher.

Armed with that knowledge we can then build a recommendation engine both for content based as well as behavior based recommendations. In the end we look at building an application backend that exposes this database either as REST or GraphQL-API.

Interest and time permitting, we can finish the workshop with an overview of Graph Data Science, where we cover the most important graph algorithms (e.g. centrality or clustering) and explain basic projections.