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Semester 1 - Dual-Delivery
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This subject teaches fundamentals of data analysis as relevant to modern biomedical engineering, in an integrated approach that combines theory with highly contextualised, project-based learning. Students are introduced to the foundations of probability and random variables, statistical hypothesis testing, linear and nonlinear regression, data classification and dimensionality reduction techniques. Each topic is explicated via case studies from clinical, industrial and research applications of biomedical engineering, covering topics in biomechanics, biosensors, bioinformatics, biomedical imaging and biomaterials.
Intended learning outcomes
On completion of this subject, students should be able to:
- Calculate and interpret probabilities, probability densities, means, variances and covariances using axioms of probability, random variables and Bayes' rule
- Formulate and perform appropriate statistical hypothesis tests on biomedical engineering datasets
- Apply standard statistical procedures using a statistical computing package
- Model biomedical engineering data using linear and nonlinear models by estimating and testing hypotheses of model parameters
- Apply classifiers to biomedical engineering datasets
- Apply dimensionality reduction methods, including PCA and ICA, to biomedical engineering datasets.
- Ability to communicate effectively, with the engineering team and with the community at large
- Ability to manage information and documentation
- Understanding of professional and ethical responsibilities, and commitment to them
- Ability to function effectively as an individual and in multidisciplinary and multicultural teams, as a team leader or manager as well as an effective team member
- Ability to undertake problem identification, formulation and solution
- Ability to utilise a systems approach to design and operational performance
- Understanding of the principles of sustainable design and development
- Capacity for independent critical thought, rational inquiry and self-directed learning
Last updated: 4 September 2021