Advanced data analytics for engineers

The unit consists of essential components required to develop an advanced data analytics framework in engineering settings. It first sets up the probabilistic foundations for data analysis, including topics such as probability, random variables, expectation, key probability distributions, conditional distributions, hypothesis testing and statistical correlation, then applies these techniques to inspect and assess real-world datasets in an exploratory manner. The second part of the unit covers mainstream machine learning methods (eg neural networks and tree-based models) to perform statistical inference in regression and classification analysis.

The material will be taught in the context of real engineering problems drawn from multiple disciplines. You will be allocated to a group for a semester-long project to build your own data analytics framework, a skill that is increasingly important across all engineering disciplines.