Course overview
This course aims to equip students with essential concepts of probability theory and statistics, proficiency in applying sampling methods, statistical techniques, data visualisation, and software tools to perform analysis of research and engineering data in real-life situations, and foundational knowledge of protocols, ethics, and procedures for collecting, processing, using, and communicating different forms of data. The course will also introduce basis of modern techniques for data-driven decision support, including machine learning, big data analytics, and model-based optimisation, in research and engineering applications.
Course learning outcomes
- Critically review data requirements, ethics implications, and data management strategies for real life engineering problems and research projects
- Design and interpret exploratory data models and analysis
- Identify and apply appropriate data processing methods and statistical analysis models for hypotheses testing on real life engineering and research datasets
- Critically review, present, and report on the results of data analysis
- Identify and interpret modern data analytics techniques for decision support in research and engineering applications
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