Digital Twins are transforming how organizations monitor physical systems, simulate future behaviour, improve reliability, and make better operational decisions. This course equips learners with practical knowledge to design, simulate, validate, and apply digital twins across engineering and industrial scenarios.

You will begin by exploring digital twin architectures, reference models, maturity levels, fidelity, and the differences between digital models, digital shadows, and digital twins. Next, you will build simulation models using discrete-event simulation, agent-based modeling, system dynamics, and physics-based techniques with Python tools such as SimPy and SciPy. The course concludes with live sensor synchronization, predictive maintenance, what-if analysis, hybrid digital twins, and enterprise considerations such as governance, interoperability, security, and scalability. You will also complete a capstone project that brings these concepts together in an end-to-end digital twin workflow. By the End of This Course, You Will Be Able To: - Explain digital twin architectures, fidelity, and maturity levels. - Distinguish digital models, digital shadows, and digital twins. - Build and select appropriate simulation models. - Calibrate, verify, and validate digital twin models. - Apply sensor data, predictive maintenance, and what-if analysis. - Build and evaluate an end-to-end digital twin. Designed for engineers, developers, data professionals, and operations practitioners, this course provides a practical pathway from digital twin concepts to real-world implementation. Move from understanding digital twins to building, validating, and applying them in practice.