Description
Data analytic skills are core for accounting practitioners in our data-intensive business environment. In this subject, we build on advanced data and analysis concepts to develop an analytical mindset where students learn to frame complex managerial questions, assemble the data, compute relevant metrics and models, identify actionable insights, and design effective and efficient communication of the outcomes. Students explore and apply these skills in a variety of contexts, including management accounting, tax, audit and assurance, and financial statement analytics to develop practical skills in working with multiple analytics tools and develop skills in critically evaluating which tool is best suited for a particular problem or question. Finally, students will explore how to best interpret and communicate the results from data analytic procedures using visualisations.
Subject details
| Type | Postgraduate |
| Code | ACCT71-306 |
| EFTSL | 0.125 |
| Faculty | Bond Business School |
| Semesters offered |
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| Credit | 10 |
| Study areas |
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| Subject fees |
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Learning outcomes
1. Explain the role of data analytics for decision making in accounting. 2. Apply advanced quantitative methods and techniques to collect and analyse financial and non-financial accounting data. 3. Review, interpret, and critically evaluate results from data analytics procedures including hypothesis testing in an accounting context. 4. Demonstrate the ability to reflect and apply relevant feedback from others to an accounting analytics project. 5. Communicate complex accounting analytics information in a clear, concise writing style tailored to a professional audience. 6. Articulate complex accounting analytics information to a diverse professional audience using appropriate visual aids.
Enrolment requirements
| Requisites: ? | Nil |
|---|---|
| Assumed knowledge: | Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent): |
| Restrictions: ? | Nil |
Subject dates
Future offerings not yet planned.