Data analytic skills are core for accounting practitioners in our data-intensive business environment. In this subject we build on fundamental data and analysis concepts to develop an analytical mindset where students learn to frame 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. Finally, students will learn how to best interpret and communicate the results from data analytic procedures using visualisations.
|Faculty||Bond Business School|
1. Explain the role of data analytics for decision making in accounting. 2. Apply quantitative methods and techniques to collect and analyse financial and non-financial accounting data. 3. Interpret results from data analytics procedures including hypothesis testing in an accounting context. 4. Demonstrate the ability to apply relevant feedback from others to an accounting analytics project. 5. Communicate accounting analytics information in a clear, concise writing style tailored to a professional audience. 6. Articulate accounting analytics information to a diverse professional audience using appropriate visual aids.
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):
Students must have completed 120 credit points prior to enrolling.
Future offerings not yet planned.