Bioengineering Data Analytics

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.

• 1Calculate and interpret probabilities, probability densities, means, variances and covariances using axioms of probability, random variables and Bayes' rule
• 2Formulate and perform appropriate statistical hypothesis tests on biomedical engineering datasets
• 3Apply standard statistical procedures using a statistical computing package
• 4Model biomedical engineering data using linear and nonlinear models by estimating and testing hypotheses of model parameters
• 5Apply classifiers to biomedical engineering datasets
• 6Apply dimensionality reduction methods, including PCA and ICA, to biomedical engineering datasets.