BAN223 - Accounting Analytics and Financial Modelling

Credit Points: 15 credit points

Workload: 36 hours

Prerequisite: BAN211 Business Analytics; BDA112 Data Science Fundamentals

Co-requisite: N/A

Aims & Objectives

This is a second-year Core Unit in the Bachelor of Business, Major in Accounting Analytics and Financial Modelling, and offered as a recommended Elective Unit from another specialisation stream in the Bachelor of Business, subject to meeting pre- and co-requisites. For Course Learning Outcomes and further information relating to Bachelor of Business programs, please visit our website: http://www.mit.edu.au/study-with-us/programs/bachelor-business.  

Accounting Analytics and Financial Modelling examines how financial statement data, financial market data and non-financial metrics are associated with firm value and performance. This unit involves students building on fundamental data and analysis concepts to develop an analytical mindset where they 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 will explore and apply these skills in a variety of contexts, including accounting, finance, and data analytics, to develop practical skills and facilitate decision-making through multiple analytics tools such as Excel, Tableau, and Power BI. Finally, students will learn data mining, visualisation, optimisation, and regression analysis using accounting and financial data. 

Unit topics include: 

  • Using Data Analytics to Address Accounting Questions   
  • Master the Data: An Introduction to Accounting Data   
  • Master the Data: Data Types Used in Accounting   
  • Master the Data: Preparing Data for Analysis   
  • Perform the Analysis: Types of Data Analytics   
  • Share the Story   
  • Capstone Projects Using the AMPS Model   
  • Tax and Audit Data Analytics 
  • Managerial Accounting Analytics   
  • Financial Statement Analytics   

Learning Outcomes

  1. Course learning outcomes 

  • The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology's website: www.mit.edu.au   

  1. Unit learning outcomes  

  • Understand and apply the core principles of and approaches to data analytics in the context of managerial accounting and finance.  
  • Apply quantitative methods and techniques to collect, analyse and interpret financial and non-financial accounting data in a professional and ethical manner. 
  • Explain the role of data analytics for decision-making in accounting and finance. 
  • Develop skills associated with data mining and machine learning. 
  • Articulate and communicate accounting analytics information concisely to a diverse professional audience using appropriate and secure visual aids. 

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed 
1. Assessment 1: (Individual) - Quiz Test Week 3 - 10%  a
2. Assessment 2: (Individual) - Crypto Report  Week 8  30%  - a-d 
3. Assessment 3: Contribution and Participation  Week 12  - 10% a-e 
4. Assessment 4: (Group of 2-3 students) – Project Report (3,000 words) (30%) and Presentation (20%) Week 12  30%  20% a-e 
TOTALS   60%  40%   

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

Contribution and Participation (10%)

This unit has class participation as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules- policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). 

Further details on the type of assessment tasks and the marking rubrics will be provided in the assessment specification

Textbook and Reference Materials

Note: Students are required to purchase the prescribed textbook and have it available each week in class. 

Prescribed Text Book

  • Richardson, V., Katie L. Terrell, Ryan A. Teeter (2023), Introduction to Data Analytics for Accounting, 2nd Edition, McGraw Hill, 27th March 2023. 

Recommended Texts

  • Dzuranin A, Geerts G, and Lenk M (2023), Data and Analytics in Accounting: An Integrated Approach, 1st Edition, John Wiley & Sons, Inc. 
  • Smith, M. (2022), Research Methods in Accounting, Sixth Edition, SAGE Publications Ltd, Jun 2022. 
  • Mayes T, and Shank T (2020), Financial Analysis with Microsoft Excel, 9th edition, Cengage Learning, USA. 

Journal articles

  • Rana, T. et al. (2023), Handbook of Big Data and Analytics in Accounting and Auditing, Springer Nature Singapore Pte Ltd., 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore. ISBN 978-981-19-4460-4 (eBook).  

The Referencing style for this unit is APA:  

See the MIT Library Referencing webpage: https://library.mit.edu.au/referencing/APA and the Unit Moodle page for additional referencing support material and web links. 

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

Legend

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.