AI and Data in Business
Knowledge Graphs and Graph Analytics
This in-depth course develops directly applicable capability in Knowledge Graphs and Graph Analytics. It connects Graph Data Modeling, Graph Construction and Quality, and Query and Analytical Methods to the decisions, controls, and activities participants need to perform in their workplace.
Overview
Practical learning for workplace transfer.
This in-depth course develops directly applicable capability in Knowledge Graphs and Graph Analytics. It connects Graph Data Modeling, Graph Construction and Quality, and Query and Analytical Methods to the decisions, controls, and activities participants need to perform in their workplace. The five-module curriculum progresses toward Graph Investigation Workshop, using evidence, scenarios, and work products appropriate to the subject.
Prerequisites
Relevant workplace familiarity is recommended; exact technical prerequisites can be adapted to the participant group.
Objectives
- Analyze graph data modeling, including nodes, relationships, properties, labels, and identifiers.
- Configure or structure graph construction and quality, including entity resolution, deduplication, and relationship extraction.
- Evaluate query and analytical methods, including cypher and graph traversal patterns.
- Manage enterprise graph applications, including fraud rings, supply networks, asset dependencies, and customer 360.
- Apply graph investigation workshop, including build a graph model from a multi-entity scenario.
Target audience
- Professionals responsible for this subject area
- Managers, supervisors, and team leaders
- Analysts, specialists, engineers, or coordinators working with the relevant processes
- Project, implementation, assurance, or improvement team members
- Professionals preparing for broader responsibilities in this field
Program outline
A clear structure for the learning journey.
Outline points are grouped in one designed block instead of being treated as separate module cards.
01Graph Data Modeling3 topics
- Nodes, relationships, properties, labels, and identifiers
- Ontology, taxonomy, and schema design trade-offs
- Mapping relational and document sources into graph structures
02Graph Construction and Quality3 topics
- Entity resolution, deduplication, and relationship extraction
- Provenance, confidence scores, and temporal relationships
- Validation rules and graph-quality measurements
03Query and Analytical Methods3 topics
- Cypher and graph traversal patterns
- Centrality, communities, paths, and similarity algorithms
- Performance implications of indexes and traversal depth
04Enterprise Graph Applications3 topics
- Fraud rings, supply networks, asset dependencies, and customer 360
- Knowledge graphs for RAG and semantic search
- Access controls and sensitive relationship handling
05Graph Investigation Workshop3 topics
- Build a graph model from a multi-entity scenario
- Run path, neighborhood, and community analyses
- Interpret findings and communicate limits of the evidence
Materials provided
- ○ Course-specific presentation slides
- ○ Guided exercises, scenarios, or configured-environment activities appropriate to the subject
- ○ Course-specific worksheets, checklists, or calculation templates
- ○ Applied workplace case materials
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for implementation questions
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy adapts the program to the client’s operating environment. Delivery combines structured explanation with subject-specific analysis, exercises, and implementation decisions so participants can transfer the learning to real responsibilities without implying vendor authorization.
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