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Unit of study_

ACCT3015: Data Analytics for Accounting

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

This unit provides an overview of data analytics for accounting. Traditional accounting techniques and practices were developed in the industrial era (a low information environment) and have changed very little since. Financial reports tend to be historical and financial in nature, heavily aggregated, static and paper based. This unit considers how the mega-trend of 'Big Data', artificial intelligence and robotics is impacting and shaping current accounting, financial reporting and auditing practices and their likely impact on future practices. This UoS also explores how Big Data and artificial intelligence is currently used in accounting practice; and the potential of these techniques to shape future practices in specific areas such as accounting measurement and forecasting, audit sampling and the timing and frequency of reporting (as examples). The unit also introduces students to current accounting research in the field of Big Data and artificial intelligence.

Unit details and rules

Unit code ACCT3015
Academic unit Accounting
Credit points 6
ACCT2011 and (ACCT2012 or ACCT2019)
Assumed knowledge

Completion of INFS3110 (or INFS2001) is desirable.

Available to study abroad and exchange students


Teaching staff

Coordinator Stewart Jones,
Lecturer(s) Nurul Alam,
Jian Ho,
Paul Blayney,
Stewart Jones,
Eric Clubb,
Emily Neo,
Hanieh PoostchiMohammadabadi,
Ravi Seethamraju,
Type Description Weight Due Length
Final exam (Record+) Type B final exam hurdle task Final Exam
Written and numerical questions
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Assignment
Written Task
Due date: 27 Nov 2020 at 23:59
2500 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
In-semester test (Record+) Type B in-semester exam Mid-semester exam
Written and numerical questions
25% Week 07
Due date: 17 Oct 2020 at 14:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
hurdle task = hurdle task ?
Type B final exam = Type B final exam ?
Type B in-semester exam = Type B in-semester exam ?

Assessment summary

  • Final exam: This assessment is listed as MANDATORY which means you must undertake the assessment and achieve at least 45% of the available marks in that assessment. Students who fail to achieve this minimum standard in this assessment, even when their aggregate mark for the entire unit of study is above 50%, will be given a Fail grade for this unit. As a result a student's academic transcript will show a Fail grade and the actual mark achieved if the final mark of the unit is between 0-49 and a Fail grade and a capped moderated mark of 49 for all other final marks.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 


75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.


65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.


50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to the unit: Data Analytics in Accounting / Big Data, Artificial Intelligence/Automation/Robotics – Implications to Accounting and Auditing Lecture and tutorial (3 hr)  
Week 02 Data Issues in Accounting – Big Data Sources Lecture and tutorial (3 hr)  
Week 03 Data Issues in Accounting – Enterprise Level Lecture and tutorial (3 hr)  
Week 04 Data Issues in Auditing Lecture and tutorial (3 hr)  
Week 05 Introduction to Analytical Tools: Excel Financial Modeling Methods Lecture and tutorial (3 hr)  
Week 06 Data Management with Excel Lecture and tutorial (3 hr)  
Week 07 Data Extraction & Filtering with Excel Lecture and tutorial (3 hr)  
Week 08 Introduction to Excel VBA Lecture and tutorial (3 hr)  
Week 09 Introduction to Machine learning analysis Lecture and tutorial (3 hr)  
Week 10 Alternative machine learning methods Lecture and tutorial (3 hr)  
Week 11 Alternative machine learning continued Lecture and tutorial (3 hr)  
Week 12 Revision Lecture and tutorial (3 hr)  

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Understand and interpret various accounting issues in the context of Big Data and the 4th industrial revolution. Students will learn how to use and apply advanced analytical tools and master the basics of Big Data analytical tools such as machine learning.
  • LO2. Analyse and critically evaluate highly complex real data problems within real-world constraints and critically evaluate the advantages and limitations of various analytical tools.
  • LO3. Identify appropriate data analytic and machine learning tool(s) for business decision making within the context of accounting and Big Data issues. Learn how to harness high-velocity of information and apply data analytics skills to improve effectiveness, efficiency and efficacy in various problem solving tasks.
  • LO4. Develop verbal and written communication skills within the context of accounting and Big Data considerations. Learn how to effectively interpret the outputs of various data analytic techniques and communicate those outputs to decision makers.
  • LO5. Develop ethics and privacy principles and social skills within the context of accounting and Big Data issues; including the interpretation and application of various data analytic outputs.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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