Understanding Graph Data

Graph data representation delivers strong capabilities in customer profiling, intelligence, security and many other fields - everywhere where you can find interconnected or cross-referenced entities. And in the field of AI, progressing from Retrieval-Augmented Generation (RAG) to GraphRAG (Microsoft:  Welcome to GraphRAG )can bring performance of a Large Language Model (like GPT-5) over the threshold of practical usability.

There are good online courses on using different graph databases, that are recommended for technical staff involved. However before the technical staff can start coding, everyone including higher management, Business Architects & Analysts, and in some cases even end-users, together with the technical staff, need to learn to see data as graphs. 

This course is designed to bring the teams together, give them common understanding and common language, ensuring effective project definition, analysis and progress through development. 

Training schedule

While a course is always customised to your needs, the following is used as the starting point for customisation:

Day 1: Graph Fundamentals 

Attendance of everyone is expected, including management/executives

• Introduction to graph data

• Lab: Identifying graphs in data

• Graph analytics overview

• Lab: experimenting with graph analytics

Day 2: Graph Schema Definition

Attendance of everyone is expected, management/executives optional

• Graph database schema (customised to fit the client's platform)

• Lab: modelling 

Day 3: Graph queries

Mostly for technical staff, and hands-on analysts. 

• Introduction to query language: navigation and pattern-matching

• Lab: running basic queries

• Defining models for queries

• Lab: review and optimise the model produced on Day 2. Write queries. 

Day 4: Using graph data programmatically (optionial)

Technical staff only. Use the database of choice

• Lab: Work with created database in Python