Outline

Overview

This unit will consolidate and enhance skills in accounting analytics. It will allow students to develop and apply more advancedaccounting analytics skills utilising and applying a business intelligence tool to perform data analysis and visualisation. It willincorporate more advanced aspects of analytics ensuring more extensive use of business intelligence tools and applicationtools (PowerBI) for applying predictive analytics in several industry contexts. It will also create opportunities for strategicapplication of prescriptive analytics in accounting operational areas including auditing, financial accounting and managerialaccounting. Students will have multiple opportunities to draw business insights from data sets to develop businessperformance enhancements and embed decision-making through process automation. The units will explore and reflect onpotential ethical issues in developing prescriptive analytics. Skills will be developed via a case study approach, based around avariety of business types, such as: services, banking and finance, eRetail, lean manufacturing and transport and logistics.Emphasis will be placed on the drivers of success in such business types when applying analytics and process automation.

Unit details and rules

Unit code	ACCT6029
Academic unit	Accounting
Credit points	6
Prohibitions ?	None
Prerequisites ?	ACCT6001 and ACCT6008 and ACCT6019
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Nurul Alam, nurul.alam@sydney.edu.au
Lecturer(s)	Nurul Alam, nurul.alam@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam Written exam	45%	Formal exam period	1.5 hours
Outcomes assessed:
Assignment	Case study 1 Group assignment 1	15%	Week 06 Due date: 28 Mar 2024 at 23:59	1500 words
Outcomes assessed:
Supervised test ?	In-semester test Written exam	25%	Week 07 Due date: 08 Apr 2024 at 09:40	1 hour
Outcomes assessed:
Assignment	Case study 2 Group assignment 2	15%	Week 12 Due date: 17 May 2024 at 23:59	1500 words
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Group assignment 1: This assessment will be due in week 6.
Group assignment 2: This assessment will be due in week 12.
Mid-semester exam: The mid-semester exam will be conducted in week 7, covering materials from weeks 1-5 (lectures/tutorials) inclusive.
Final exam: The exam will contain direct questions on material from weeks 6-13, although this will require background knowledge and skills from weeks 1-13. The purpose of the exam is to test your personal learning on advanced aspects of analytics in several industry contexts. The exams will contain theoretical, discussion and application questions, allowing students to demonstrate their knowledge and skills to answer questions and solve problems, consistent with the learning goals of this unit. The final exam is listed as having a HURDLE TASK.
An assessment that is listed as HURDLE TASK means you must undertake the assessment and achieve a mark above a minimum standard. Students who fail to achieve this minimum standard in this assessment, even when their aggregate mark for the entire unit is above 50%, will be given a Fail grade for the unit. As a result the student's academic transcript will show a fail grade and the actual mark achieved if between 0-49 and a fail grade and a capped moderated mark of 49 for all other marks. The hurdle mark for this assessment is 45%.

Detailed information for each assessment can be found on Canvas.

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	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 sydney.edu.au/students/guide-to-grades.

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.

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 penalties apply in accordance to the University of Sydney Business School Late submission policy

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.

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 2023 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 2023. 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	Lecture (1 hr)
Week 01	Week 1 tutorial	Tutorial (2 hr)
Week 02	Data issues/sources of data	Lecture (1 hr)
Week 02	Week 2 tutorial	Tutorial (2 hr)
Week 03	Machine learnings methods	Lecture (1 hr)
Week 03	Week 3 tutorial	Tutorial (2 hr)
Week 04	Business use applications for machine learning methods	Lecture (1 hr)
Week 04	Week 4 tutorial	Tutorial (2 hr)
Week 05	Credit risk modelling/Going concern evaluations	Lecture (1 hr)
Week 05	Week 5 tutorial	Tutorial (2 hr)
Week 06	Fraud detection	Lecture (1 hr)
Week 06	Week 6 tutorial	Tutorial (2 hr)
Week 07	Mid-semester exam week	Lecture (1 hr)
Week 07	Week 7 tutorial	Tutorial (2 hr)
Week 08	Stock price and stock crash risk forecasting	Lecture (1 hr)
Week 08	Week 8 tutorial	Tutorial (2 hr)
Week 09	Mergers and acquisitions	Lecture (1 hr)
Week 09	Week 9 tutorial	Tutorial (2 hr)
Week 10	Earnings forecasting	Lecture (1 hr)
Week 10	Week 10 tutorial	Tutorial (2 hr)
Week 11	Climate risk disclosures and corporate sustainability	Lecture (1 hr)
Week 11	Week 11 tutorial	Tutorial (2 hr)
Week 12	Ethical considerations of data science in business	Lecture (1 hr)
	Week 12 tutorial	Tutorial (2 hr)
	Week 13 tutorial	Tutorial (2 hr)
Week 13	Course summary and revision	Lecture (1 hr)

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. Apply more advanced accounting analytics skills utilising and applying a business intelligence tool to perform data analysis/visualisation.
LO2. Apply business intelligence tools for predictive analytics.
LO3. Apply prescriptive analytics strategically in accounting activities and for business performance enhancement through process automation.
LO4. Explore and reflect on the ethical issues of prescriptive analytics.
LO5. Analyse business success drivers in a diverse range of industries.

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.

This unit of study is running for the first time in 2024

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

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

Media

Student links

About us

Connect

Current students

ACCT6029: Applied Business Performance Management

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

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

Media

Student links

About us

Connect