- Graph theory and the elements that make a graph
- Graph structures
- Data model and data
- Reading data from the graph
- Writing data to the graph
- Graph data model
- Modeling nodes and creating nodes for an instance model.
- Modeling relationships and creating relationships for an instance model.
- Testing the graph data model.
- Refactoring a graph data model and how labels help.
- Eliminating duplicate data in the graph.
- Using specific relationship types.
- Adding intermediate nodes.
- Filtering queries
- Controlling results returned
- Working with Cypher data
- Graph traversal
- Pipelining queries
- Subqueries
- Using parameters
- Profile and explain how aggregation works at runtime
- Aggregations: Using collect(), count()
- Using pattern comprehension
- Working with lists
- Functions that return a single value
- Functions that return lists
- Element type transformations
- List selection predicates
- List comprehension
- Use aggregating functions: sum(), avg()/stddev(), min()/max(), percentages and percentiles
- Indexes and constraints to improve query and load performance.
- How to create and use indexes, constraints, control and better understand the use of indexes in Neo4j.
- The lifecycle of the Neo4j Driver and how it relates to your application
- Instal and instantiate the Neo4j Python Driver to your Python project
- How read and write transactions work with Neo4j
- Best practices on how to use Neo4j within your Python project.