ACX3300 Predictive analytics in accounting
Faculty of Business and Economics
ACX3300 Predictive analytics in accounting is a level 3, 6-credit-point, undergraduate unit from the Faculty of Business and Economics, offered in 2022 in Semester 1 at Caulfield. It needs (ETC1000, ETF1100, ETX1100 or ETB1100) and (ACC1100, ACF1200, ACF1100, ACX1100, ACB1120, ACB1020, ACX1200 or ACC1200).
- Credit points
- 6
- Offered in 2022
- Semester 1
- Caulfield
- Assessment
- No exam
- 1 task
- Workload
- 144 hours
- per semester
This is the 2022 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before ACX3300
Prerequisites
Pass these before you enrol.
- ACC1100Introduction to financial accountingNo reviews yet
- ACF1200Accounting for managersNo reviews yet
- ACF1100Introduction to financial accountingNo reviews yet
- ACX1100Introduction to financial accountingNo reviews yet
- ACB1120Financial accounting 1No reviews yet
- ACB1020Accounting in businessNo reviews yet
- ACX1200Accounting for managersNo reviews yet
- ACC1200Accounting for managersNo reviews yet
After ACX3300
No unit lists ACX3300 as a prerequisite in the 2022 handbook.
Enrolment rules
To be successful in this unit, background knowledge and application of maths is required at the equivalent of VCE Year 12 level. You may have satisfied this by completing relevant prerequisite unit/s, or you have covered relevant topics in your final years of secondary study. You should self-assess your maths competency prior to enrolling in this unit.
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 basic SQL coding skills and SAS statistics software necessary to process and analyse Big Data. It will cover three applications of predictive analytics using accounting data: (1) forecasting future earnings; (2) predicting accounting fraud; and (3) predicting bankruptcy. The unit will be delivered in computer labs to allow you to code and work on data in real-time.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Caulfield | On campus |
Assessment
- Within semester assessmentThreshold hurdle100%
Learning outcomes
When you finish this unit, you should be able to:
- 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 and teaching
- Tutorials18 hours
- Lectures18 hours
- Teaching approachProblem-based learning
- Teaching approachActive learning
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 and practice, assessment and preparation for scheduled activities. The unit requires on average three hours of scheduled activities per week. Scheduled activities may include a combination of teacher-directed learning, peer directed learning, and online engagement.
This unit includes problem-based learning approaches, where students engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
This unit engages students in actively applying their knowledge, skills and attributes in interactive, collaborative and reflective activities.
Learning resources
Required resources
SAS Essentials: Mastering SAS for Data Analytics, 2nd edition, by Alan Elliott and Wayne Woodward. 2015, John Willy & Sons, New York.
Technology resources
SAS software
Where it fits
ACX3300 is part of 2 areas of study in the 2022 handbook.
Contacts
- Chief Examiners
- Professor Wen He
Common questions
What are the prerequisites for ACX3300?
You need (ETC1000, ETF1100, ETX1100 or ETB1100) and (ACC1100, ACF1200, ACF1100, ACX1100, ACB1120, ACB1020, ACX1200 or ACC1200) before you enrol. Enrolment rules also apply.
When is ACX3300 offered?
In 2022, ACX3300 runs in Semester 1 at Caulfield.
How much work is ACX3300?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ACX3300 have an exam?
No. ACX3300 has 1 assessment task and no exam.
Which majors and minors include ACX3300?
ACX3300 is part of Accounting.