Outline

Overview

Traditional accounting techniques and practices have changed little since their development in the industrial era (a low information environment). Financial reports tend to be historical and financial in nature, heavily aggregated, static and paper based. This unit of study introduces students to current accounting research to consider how the mega-trend of 'Big Data', artificial intelligence and robotics is shaping current accounting, financial reporting and auditing practices and their likely future impact. The unit also explores specific aspects of how Big Data and artificial intelligence are currently used in accounting practice and their potential to shape future practices in specific areas such as accounting measurement and forecasting, audit sampling and the timing and frequency of reporting (as examples).

Unit details and rules

Academic unit	Accounting, Governance and Regulation
Credit points	6
Prerequisites ?	ACCT2011 and (ACCT2012 or ACCT2019)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Completion of INFS3110 (or INFS2001) is desirable
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Paul Blayney, paul.blayney@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam Written and numerical questions. Further details provided on Canvas.	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written test ?	Mid-semester exam Written and numerical questions. Further details provided on Canvas.	25%	Week 07 Due date: 20 Sep 2025 at 15:40	1 hour	AI prohibited
Outcomes assessed:
Written work	Assignment Written Task. Further details provided on Canvas.	25%	Week 13 Due date: 07 Nov 2025 at 23:59 Closing date: 17 Nov 2025	n/a	AI allowed
Outcomes assessed:
= hurdle task ?

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.
In-semester exam: This assessment will be a written test conducted outside of normal class time. See Canvas for details.
Assignment: This assessment consists of two parts. An Excel component and a Machine Learning component. See Canvas for details.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (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	Description
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.
Distinction	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.
Credit	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.
Pass	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.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard policies for late submission will apply.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to the unit: Data Analytics in Accounting / Big Data, Artificial Intelligence/Automation/Robotics – Implications to Accounting and Auditing	Workshop (3 hr)
Week 02	Data Issues in Accounting – Big Data Sources	Workshop (3 hr)
Week 03	Data Issues in Accounting – Enterprise Level	Workshop (3 hr)
Week 04	Data Issues in Auditing	Workshop (3 hr)
Week 05	Introduction to Analytical Tools: Excel Modeling Methods	Workshop (3 hr)
Week 06	Data Management with Excel Data Forecasting Using Exponential Smoothing	Workshop (3 hr)
Week 07	Data Extraction & Filtering with Excel	Workshop (3 hr)
Week 08	Introduction to Excel VBA Excel’s Get & Transform Methods (linking Excel data to the web)	Workshop (3 hr)
Week 09	Introduction to Machine Learning Analysis I	Workshop (3 hr)
Week 10	Introduction to Machine Learning Analysis II	Workshop (3 hr)
Week 11	Alternative machine learning methods	Workshop (3 hr)
Week 12	Alternative machine learning continued	Workshop (3 hr)
Week 13	Revision	Workshop (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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

Student feedback has provided the impetus for several minor enhancements have been made to this unit since its last offering.

Additional information

Disclaimer

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

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

ACCT3015: Accounting Data Analytics

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links