MBA613 - Accounting Analytics

Credit Points: 20 credit points

Workload: 60 hours

Prerequisite: MDA611 Predictive Analytics

Co-requisite: N/A

Aims & Objectives

This is an elective unit out of a total of 12 units in the Master of Business Analytics (MBA). This unit addresses the course learning outcomes and complements other units in a related field by developing students’ specialised knowledge of business analytic tools and technologies in the important field of accounting. For further course information refer to: This unit is part of the AQF level 9 courses.

Students will develop data analytic skills to address the needs of the accounting profession in the Information Age. They will develop skills necessary for analytic-minded accountants in a data-filled world. These skills include: recognising when and how data analytics can address accounting questions; understanding the process needed to extract (query), clean and prepare data for analysis; comprehending what is meant by data quality, be it completeness, reliability, or validity; performing basic analysis to understand the quality of the underlying data and their ability to address the business question; implementing an approach for drawing conclusions and make recommendations using statistical data analysis; and reporting results of analysis using data visualisation and data reporting tools.

This unit will cover the following topics:

  • An analytics mindset
  • Data scrubbing and data preparation
  • Data quality
  • Descriptive data analysis
  • Data analysis through data manipulation
  • Problem solving through statistical data analysis
  • Data visualisation and data reporting

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit in respect of the course being studied are listed on the Melbourne Institute of Technology website: 

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:

  1. Explain the importance of data analytics in contemporary business contexts and develop an analytical mindset.
  2. Identify, collect and analyse financial and non-financial accounting data utilizing quantitative methods and techniques.
  3. Analyse, interpret, and apply results from data analysis procedures in an accounting context.
  4. Select the appropriate analytical methods for specific accounting purposes to generate insights.
  5. Communicate actionable insights to a professional audience using appropriate data visualisation and reporting tools.

Weekly Topics

This unit will cover the content below:

Week Topics
1 The role of accounting in business, and the statement of financial position
2 Data Analytics for Accounting and Identifying the Questions
3 Mastering the Data
4 Perform the Test Plan and Analyzing the Results
5 Communicating Results and Visualisations
6 The Modern Accounting Environment
7 Audit Data Analytics
8 Managerial Analytics
9 Financial Statement Analytics
10 Tax Analytics
11 Accounting Analytics Case Studies
12 Revision


Assessment Task Assessment Method Due Date Release Date A B Learning Outcomes Assessed
Assignment 1 Accounting analysis case study and written report Week 3 Week 1 10%   b
In-class test Application of Weeks 1-5 topics covered Week 6     10% a
Assignment 2 Group project using real-world accounting dataset Week 11 Week 7 30%   c-d
Laboratory and Problem Based Learning participation & submission Accounting Analytics cases discussions and software-based lab Week 2-11 Week 2-11 10%   a-e
Final Examination (3 hours) Application of unit topics covered       40% a-e
TOTALS       50% 50%  

Task Type: Type A: unsupervised, Type B: supervised.

Contribution and participation (in class) (10%)
Students are expected to attend each scheduled session, arrive on time and remain for the entire session. Adherence to this requirement will be reflected in the marks awarded for this assessment. Students are also strongly encouraged to actively participate in the class discussions and tutorial activities by answering questions, expressing their opinions, insights and their learnings from the course.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials

Prescribed Textbook:

  • Vernon Richardson and Katie Terrell and Ryan Teeter (2021), Introduction to Data Analytics for Accounting, 2nd ed., McGraw-Hill.

Other recommended references:

  • Gary A. Porte and Curtis L. Norton, (2017), Financial Accounting: The Impact on Decision Makers, 10th edition Cengage Learning, US.
  • Gowtorpe, C., (2018), Business Accounting and Finance, 4th edition, South-Western Cengage, U.S.A.
  • Nobles, Mattison, Matsumura, Best, Fraser, Tan, Willett (2015), Horngren's Financial Accounting, 8th edition, Pearson, Australia.
  • Bazley, M., Hancock, P., Berry, A. and Jarvis, R., (2010), Contemporary Accounting: A Conceptual Approach, 7th edition, Nelson, Melbourne.
  • Birt, J. et al. (2014), Accounting: Business Reporting for Decision Making, 5th edition, Wiley, Australia.
  • Jackling, B., Raar, J., Wigg, R., and Williams, B., (2010), Accounting: A Framework for Decision Making, 3rd ed., McGraw-Hill, Sydney.


  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Foundations and Trends in Machine Learning
  • Journal of Machine Learning Research
  • Artificial Intelligence
  • International Journal of Machine Learning and Cybernetics
  • International Journal of Artificial Intelligence
  • Intelligent Data Analysis
  • Applied Artificial Intelligence
  • Journal of Experimental and Theoretical Artificial Intelligence
  • Journal of Artificial Intelligence

Adopted Reference Style: IEEE

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study


Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.