Unit outline_

SMBA6003: Data Analytics and Modelling

Semester 1, 2026 [Normal evening] - Castlereagh St, Sydney

One of the most significant developments associated with the digital revolution is the increased availability of data. For managers and leaders in contemporary organisations, the ability to effectively analyse and draw useful inferences from data is critical. It is also important that managers can communicate complex interrelationships found in the data to senior management in a way that maximises the possibility that it can lead to favourable and sustainable change. Access to and use of data is critical to organisations in their need to effectively respond to a more volatile economic and financial environment, and Government intervention and regulation.Superior data analytic and modelling capabilities are increasingly seen as a source of competitive advantage, both for business and for employees working within business. This unit of study can deliver this competitive advantage in at least six distinct ways - (1) it will reveal the type of internal data that an organisation must compile for effective decision making; (2) it will identify the external data that must be used in combination with the internal data, and where that external data is sourced; (3) it will analyse the tools and modelling techniques that can be used to draw timely and relevant insights from a range of different forms of data; (5) it will examine how these tools and modelling techniques can be practically applied across a range of organisational settings; and (6) it will demonstrate how any findings should be communicated to time-poor senior management. As part of this unit of study, students will be given the opportunity to work with real-world data sets and case studies, and to apply those data sets to their own and other organisations.

Unit details and rules

Academic unit Management Education
Credit points 6
Prerequisites
? 
None
Corequisites
? 
SMBA6001
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator John-Paul Monck, john-paul.monck@sydney.edu.au
The census date for this unit availability is 31 March 2026
Type Description Weight Due Length Use of AI
Written exam Final exam
Short answer and essay-style answers around two questions/cases
30% Formal exam period
Due date: 27 May 2026 at 18:00
2 hours AI prohibited
Outcomes assessed: LO1 LO2
Presentation group assignment Team presentation
n/a
40% Week 10
Due date: 20 May 2026 at 18:00
3 slides & 3 minutes per person AI allowed
Outcomes assessed: LO1 LO2 LO3 LO4
Written work Individual Assignment
n/a
30% Week 11
Due date: 24 May 2026 at 23:59

Closing date: 03 Jun 2026
6 pages AI allowed
Outcomes assessed: LO1 LO2 LO3
group assignment = group assignment ?

Assessment summary

Group assignment/presentation: Students will be divided into teams of ~5. Your team will have autonomy to explore and analyse a topic or problem of your choice, with some exemplar questions provided. Each team member will receive the same mark. There will be a peer review feedback mechanism, noting that peer evaluation is a regular task of any leader and is intended to encourage equitable contributions during the course of your team work.

Individual assignment: This assessment will be based around an data analysis issue of concern in Australia or globally, with exemplar questions provided, that can otherwise be reshaped subject to discussion with the Professor.

Final exam: This exam will cover all topics of the unit.

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 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:

The standard Business School late penalties 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.

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

WK Topic Learning activity Learning outcomes
Week 01 Topic 1: Economic variables and basics of data Seminar (4 hr) LO2 LO3 LO4
Week 02 Topic 2: Economic linkages, visualization and data presentation Seminar (4 hr) LO1 LO2 LO4
Week 03 Topic 3: Understanding and modelling functions Seminar (4 hr) LO1 LO3 LO4
Week 04 Topic 4: Price / Inflation modeling and analysis Seminar (4 hr) LO1 LO3 LO4
Week 05 Topic 5: Predictive Analysis using AI Seminar (4 hr) LO1 LO3
Week 08 Topic 6: Profitability Analysis by Levers and Drivers Seminar (4 hr) LO1 LO2
Week 09 Topic 7: Price Optimization and related scenarios Seminar (4 hr) LO1 LO3 LO4
Week 10 Topic 8: Risk Measurement and Management Seminar (4 hr) LO1 LO2 LO3
Week 11 Topic 9: Performance Measurement and Benchmarking Seminar (4 hr) LO3 LO4
Week 12 Presentations and Revision Seminar (4 hr) LO1 LO2 LO4
Week 14 (STUVAC) Final Exam Assessment (2 hr) LO1 LO2 LO3

Attendance and class requirements

Lecture recordings: Note that MBA classes held at the CBD Campus are not systematically recorded, and 100% class attendance is expected for each unit of the MBA program. If there are extenuating circumstances as to why you are not able to attend a particular class, please contact your unit coordinator as soon as possible, and also notify your group members (if the unit has a group work component). A unit requirement is 90% class attendance, where some classes are mandatory, so those who drop below this level may not pass the unit.

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.

Required readings

Students are strongly encouraged to complete the prepatory online course before coming to class, particularly in relation to mathematical and statistical grounding. This may involve up to 10 hours of online study depending on familiarity with tools such as Excel.

While we don’t prescribe a textbook, the following are useful background reading and will be referred to from time to time: 

  • Carl Bergstrom's "Calling Bullshit" ISBN-13 ‏ : ‎ 978-0525509189
  • Darrell Huff's "How To Lie With Statistics" ISBN-13 ‏ : ‎ 978-0393310726
  • Alex Edmans' "May Contain Lies" ISBN-13 ‏ : ‎ 978-0241630181

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. Develop evidence-based business recommendations to solve complex problems, drawing on appropriate data, analytical techniques and ethical considerations
  • LO2. Predict the impact of external factors emanating from the world economy on the company’s performance using appropriate modelling techniques.
  • LO3. Communicate complex ideas to senior management using appropriate communication styles and evidence-based recommendations with empirically-defensible analytical techniques.
  • LO4. Collaborate with colleagues and peers to solve complex data-related problems using innovative tools and solutions while arriving at a consensus among peers.

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.

An online preparatory course was created for students with less experience with data analytics, which is provided as an optional resource. Additional simulation exercises have also been created, although a range of tools will be provided assuming zero background.

Students are encouraged to bring a laptop to classes.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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