ACO5170 Predictive analytics in business
Faculty of Business and Economics
ACO5170 Predictive analytics in business is a level 5, 6-credit-point, postgraduate unit from the Faculty of Business and Economics. It isn't offered in 2027. It has no prerequisites.
- Credit points
- 6
- Offered in 2027
- Other periods
- Monash Online
- Assessment
- No exam
- 2 tasks
- Workload
- 144 hours
- per semester
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Requisites
Before ACO5170
No prerequisites or corequisites besides the enrolment rules below.
After ACO5170
No unit lists ACO5170 as a prerequisite in the 2027 handbook.
Enrolment rules
You must be enrolled in course A6032, B4008, B6025, B6028 or in a Monash Online course to undertake this unit.
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 in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| Teaching period 3 | Monash Online | Mo |
Assessment
- Written50%
- Written50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 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 and teaching
- 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, 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.
This unit includes problem-based learning approaches, where you 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 you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
Contacts
- Chief Examiners
- Professor Wen He
Common questions
What are the prerequisites for ACO5170?
ACO5170 has no prerequisites, but enrolment rules apply.
When is ACO5170 offered?
ACO5170 has no offerings listed in the 2027 handbook.
How much work is ACO5170?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ACO5170 have an exam?
No. ACO5170 has 2 assessment tasks and no exam.