Systems Simulation and Engineering Data Analysis (4 Credits)
Learning Outcomes:
- Explain different types of models and various system simulation definitions
- Apply simulation methods and techniques for problems formulation in simulation models effectively
- Explain the significance of engineering data analytics within the business and manufacturing context
- Evaluate the implementation of descriptive, predictive, and prescriptive models in decision-making processes
Topics:
- Topic 1: Introduction to System, Model and Simulation
- Topic 2: Input Distributions and Random Number Generators
- Topic 3: Verification, Validation and Replication
- Topic 4: Discrete Event Simulations (DES) & Monte Carlo Simulation
- Topic 5: System Dynamics (SD)
- Topic 6: Agent Based Modelling and Simulation (ABMS)
- Topic 7: Data Mining and Big Data Analytics
- Topic 8: Descriptive Analytics
- Topic 9: Predictive Analytics
- Topic 10: Prescriptive Analytics
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