Digital Twin Implementation course
Advanced Digital Twin development techniques and applications
Digital Twin Implementation training course
Next start date: 5 February 2025
Format: Online – live sessions
Duration: 4 weeks (6 hours of active learning)
Cost: £950
This Digital Twin Implementation course moves beyond high-level concepts and dives into the technical frameworks, data architectures, and AI integration required to construct robust, scalable, and interoperable digital twins.
The course equips you with the technical and strategic know-how to design, build, and optimise intelligent digital twin systems that deliver measurable value.
𝐁𝐲 𝐭𝐡𝐞 𝐞𝐧𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐞, 𝐲𝐨𝐮'𝐥𝐥 𝐛𝐞 𝐚𝐛𝐥𝐞 𝐭𝐨:
✅ Understand how to develop a unified framework for scalable digital twin solutions
✅ Ensure interoperability and effective data management across systems
✅ Leverage AI, machine learning, and analytics to enable smarter decision-making
✅ Establish automated feedback loops for continuous improvement and long-term value creation
𝐖𝐡𝐨 𝐬𝐡𝐨𝐮𝐥𝐝 𝐚𝐭𝐭𝐞𝐧𝐝?
This course is ideal for professionals responsible for shaping digital transformation and innovation strategies within their organisations - the enablers, adopters, and translators of digital twin capabilities.- Developers and architects building next-generation solutions
- Data engineers designing data pipelines and integration frameworks
- AI/ML specialists embedding intelligence into digital twin systems
- Systems engineers establishing interoperability and standards
- Technical leads overseeing digital twin implementation programmes
COURSE OVERVIEW
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Week 1 – Developing a common framework for designing and building Digital Twins
- Principles of a unified digital twin development framework
- Standard components and reference architectures
- Industry-specific vs cross-domain design considerations
- Principles of a unified digital twin development framework
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Week 2 – Building interoperability and an approach to data management
- Interoperability frameworks and communication standards
- Data governance and lifecycle management
- Ontologies for semantic data modelling and integration
- Approaches for synchronising physical and digital entities
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Week 3 – AI and analytics in the world of digital twinning
- Role of AI and ML in digital twin intelligence
- Predictive, prescriptive, and real-time analytics
- Data pipelines for training and deploying ML models
- Use cases across industries
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Week 4 – Building a Digital Twin development plan with automated feedback and continuous value growth
- Structuring a digital twin development and deployment plan
- Feedback mechanisms for adaptive learning and optimisation
- KPIs and metrics for value realisation and growth
- Project: Designing a continuous-value digital twin system
