SMBA6009: Semester 1, 2025

Overview

This unit is developed for people interested in understanding how to create a successful strategic roadmap for the practical implementation of analytics and AI in a business context. The unit will provide you with an understanding of data, machine learning and recent AI developments (such as chat GPT) in order to enable you to lead, seize opportunities, and transform organisations (including your own) through the application of analytics and AI. For many years, state-of-the-art strategic thinking has promoted business models and leadership strategy was based on competition analysis of close sector rivals, market analytics, as well as traditional human resources processes and talent development. At the present time though, the impacts of digital transformation are so broad and developing so swiftly that the most disruptive competition is likely to be external and come from businesses that are using advanced analytical techniques about which executives may have an incomplete understanding. For example, in Finance, many traditional organizations (banks) are being challenged by small Fintech start-ups which are making better use of data and analytics to deliver an improved customer experience. This unit delivers an in-depth and practical understanding of how current business needs can be supported by analytics and AI at the cutting edge of thinking about the transformative potential of digital technology for tackling business challenges. You will learn how digital transformation (through data, analytics and AI) can be championed and practically implemented in organisations to maximise opportunity, improve customer outcomes and optimise risk-reward. No previous experience in advanced analytics or data science is necessary for this unit although familiarity with the pre–reading materials is strongly recommended.

Unit details and rules

Academic unit	Management Education
Credit points	6
Prerequisites ?	SMBA6008 or SMBA6109
Corequisites ?	SMBA6001
Prohibitions ?	None
Assumed knowledge ?	The unit assumes some familiarity with basic statistical concepts and some experience in working with data
Available to study abroad and exchange students	No

Teaching staff

Coordinator	David Grafton, david.grafton@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Presentation	Group assessment Group project on a business use case	30%	Week 10 Due date: 27 May 2025 at 18:00 Closing date: 27 May 2025	20 minutes
Outcomes assessed:
Assignment	Individual written assignment Individual project on a business use case	40%	Week 10 Due date: 27 May 2025 at 18:00 Closing date: 27 May 2025	Min 4000 words max 6000 words
Outcomes assessed:
Small continuous assessment	Weekly participation assignments Short questions activities	30%	Weekly	Varied
Outcomes assessed:
= group assignment ? = AI allowed ?

Assessment summary

Group work assignment: This assignment requires group work to address an AI – related business problem which will be set in week 2. Students can self – select their groups or be allocated to a group at random. Each group should number between 4 and 6 students There will be time allowed in class for students to develop their presentation which will be given verbally in the last week of the course. Each group will present for a maximum of 20 minutes. Marking criteria will be available from week 2.

In-class, small and continuous assessment: There are six individual weekly mini tests which will be set at the end of each lecture in weeks 2,3,4,5,6 and 7. These are designed to test students’ understanding of material covered in that week’s lecture. Submissions should be approximately 500 words in length and are best written immediately following the lecture. The deadline is the Monday following the Thursday lecture, 23.59pm.

Submitted work assignment: Individual written assignment (approx. 4,000 to 6,000 words not including references and supplementary materials) will be due 1 week after the last day of class via Canvas. The topic for the individual assignment will be provided on the 1st day of class.

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.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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:

Group work assignment: Failure to present the group work assignment will result either in 0 mark or if a formal Special Consideration is awarded, in a compulsory make -up individual assignment. In-class, small and continuous assessment and Submitted work assignment: The penalty for late submission for the mini (weekly) assignments and Individual assignment is zero mark unless special consideration is granted, in which case the assessments will need to be submitted at an agreed future date.

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.

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 AI and Machine Learning for Business	Lecture (3 hr)
Week 02	Sources of Data and Types of data in the Business world	Lecture (3 hr)
Week 03	Exploratory data analysis and Visualisation	Lecture (3 hr)
Week 04	Supervised Learning I	Lecture (3 hr)
Week 05	Supervised Learning II	Lecture (3 hr)
Week 06	Unsupervised Learning	Lecture (3 hr)
Week 07	Text Analytics and NLP	Lecture (3 hr)
Week 08	Generative AI – applications and ethical concerns	Lecture (3 hr)
Week 09	Group assessment preparation	Workshop (3 hr)
Week 10	Group assessment preparation	Presentation (3 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. Define: Students will develop ability to approach and define real-world problems in leading digital transformation as well as understand limitations, which responsible thinking brings to data-driven business models
LO2. Measure: Students will develop ability to identify appropriate measures, variables and datasets as well as to understand how to collect suitable data to minimize algorithmic bias and maximize business and customer value
LO3. Analyse: Students will develop ability to select and apply the most appropriate methods and techniques to analyse the data and solve the problem
LO4. Critically evaluate: Students will develop ability to critically evaluate existing data science methodology against business needs
LO5. Communicate: Students will develop ability to communicate AI and analytics findings for business-level understanding
LO6. Be ethical and responsible: Students will develop ability to approach all stages of the business problem-solving process in responsible and ethical manner

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 2023.

Additional information

Business School Policies

Disclaimer

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

This unit of study outline was last modified on 30 Jan 2025.

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) and automated writing tools

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

SMBA6009: Bus. Transformation Through Analytics and AI

Semester 1, 2025 [Normal evening] - Castlereagh St, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

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