ENG6001 · Advanced data analytics for engineers

Overview

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.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Continuous assessment: 100% Final grades: PGO (pass grade only) or NGO (fail)

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Assess problems from an engineering perspective and deliberate on the relevant contextual factors. Combine and apply sophisticated data analysis methods and decision-making skills to analyse industrial scenarios and make recommendations that support business growth and development.
2. Justify the use of appropriate computer modelling techniques and experimental methods, whilst ensuring model or test applicability, accuracy and limitations of the methods.
3. Collaboratively evaluate an industry scenario to solve a problem or develop an innovation.
4. Demonstrate the effective communication of the outcomes in a written and verbal format and assess the work of others.

Workload

The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.

Student experience, not official rules. Nothing here changes what the Handbook says.