ACX3300 · Predictive analytics in accounting

Overview

This unit introduces you to Big Data and predictive analytics using accounting information. It will teach you the hands-on skills to manage large-scale financial databases and build predictive models to support strategic and investment decision-making. The unit will introduce you to the use of Python programming to process and analyse Big Data. It will cover descriptive analytics, data visualization, regression analysis, machine learning, and textual analysis. The focus is on the applications of predictive analytics using accounting data: (1) forecasting future earnings; (2) predicting accounting fraud; and (3) predicting bankruptcy. You will work on real-world data to develop and validate their predictive models.

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. understand the basic characteristics and applications of Big Data in the setting of business decision making
2. apply SQL and 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
6. develop teamwork skills and present the insights from Big Data to the targeted audience.

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.