ETW3482 · Data mining and predictive modelling

Overview

This unit will cover the key concepts of predictive modelling, emphasising applications in business. You will learn data pre-processing skills for predictive modelling. You will access data from various sources and be exposed to hands-on practice by studying industrial cases. You will become acquainted with popular supervised machine learning methods and gain sufficient confidence to make decisions based on data.

Offerings

Assessment

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

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

Requisites

Learning outcomes

1. appraise the benefits of data mining, predictive modelling and operationalising analytics in decision-making
2. critically explore, clean, and transform data using appropriate techniques to prepare it for predictive modelling tasks, demonstrating a problem-solving approach to data quality and relevance
3. design, implement, and evaluate predictive models using appropriate supervised learning algorithms to support responsible and evidence-based decision-making
4. communicate insights and recommendations derived from data analysis and predictive modelling clearly and effectively to diverse audiences.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

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