ACO5170 · Predictive analytics in business

Overview

This unit introduces you to Big Data and predictive analytics using financial information. Specifically, you will learn and develop hands-on skills to manage large-scale financial databases and build predictive models that support strategic and investment decision making. Further, the unit introduces you to analytics coding and develops your skills in the use of statistics software necessary to process and analyse large datasets. It covers three applications of predictive analytics using financial data, namely: (1) forecasting future earnings; (2) predicting accounting fraud; and (3) predicting bankruptcy. The unit will be delivered online and you will learn to code and work on real financial 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

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. understand the basic characteristics and applications of Big Data in business decision making
2. apply relevant software to manage and analyse large data sets to obtain insights from accounting Big Data
3. synthesise relevant information from academic research and industry practices to conceptualise predictive models
4. employ accounting Big Data to produce predictions for corporate earnings, accounting fraud, and bankruptcy
5. critically assess and test different models and select the optimal predictive models.

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.