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Unit outline_

SMBA6009: Bus. Transformation Through Analytics and AI

MBA Session 2, 2024 [Normal evening] - Castlereagh St, Sydney

This unit is developed for people interested in understanding how to create a successful strategic roadmap in order to lead, seize opportunities, and transform organisational thinking in a digital age. For many years, state-of-the-art strategic thinking has promoted business models and leadership strategy based on competition analysis of close sector rivals, market analytics, as well as traditional human resources processes and talent development. At present time, the impacts of digital transformation are so broad that the most disruptive competition is likely to be external and come from businesses about which executives may have an incomplete understanding. For example, in Finance many traditional organizations (banks) are challenged by small Fintech start-ups. Furthermore, businesses at the moment are facing many external uncertainties such as the COVID-19 outbreak crisis. This unit delivers an in-depth 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 contemporary challenges. You will learn how digital transformation (through analytics and AI) could be championed in organisations facing risk and uncertainty. No previous experience in advanced analytics or data science is necessary for this unit.

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
The census date for this unit availability is 13 September 2024
Type Description Weight Due Length
Presentation group assignment Group assessment
Group project on a business use case
30% Week 11
Due date: 14 Nov 2024 at 22:00

Closing date: 14 Nov 2024
20 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Individual written assignment
Individual project on a business use case
40% Week 12
Due date: 22 Nov 2024 at 23:59

Closing date: 27 Nov 2024
Min 4000 words max 6000 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Small continuous assessment Weekly participation assignments
Short questions activities
30% Weekly Varied
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
group assignment = group assignment ?

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.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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

WK Topic Learning activity Learning outcomes
Week 01 Introduction to AI and Machine Learning for Business Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 02 Sources of Data and Types of data in the Business world Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 03 Exploratory data analysis and Visualisation Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 04 Supervised Learning I Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 05 Supervised Learning II Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 06 Unsupervised Learning Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 07 Text Analytics and NLP Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 09 Generative AI – applications and ethical concerns Lecture (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 10 Group assessment preparation Workshop (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 11 Group assessment preparation Presentation (3 hr) LO1 LO2 LO3 LO4 LO5 LO6

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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.

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.