Outline

Overview

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
Lecturer(s)	Rico Merkert, rico.merkert@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Individual Assignment n/a	30%	Week 09 Due date: 24 Apr 2022 at 23:59 Closing date: 24 Apr 2022	5 pages
Outcomes assessed:
Assignment	Team presentation n/a	40%	Week 10 Due date: 27 Apr 2022 at 18:00 Closing date: 27 Apr 2022	3 slides & 3 minutes per person
Outcomes assessed:
Final exam (Take-home short release)	Final exam Essay style answers around two questions/cases	30%	Week 13 Due date: 20 Apr 2022 at 18:00	2 hours
Outcomes assessed:
= group assignment ? = Type D final exam ?

Assessment summary

Group assignment/presentation: Students will be divided into teams of about 5 or 6 before the start of the first class. Your team will be allocated a topic and a company. You tasked with presenting to senior management of your company on the assigned topic. The team will receive a mark as a whole. However, there will be a peer review feedback mechanism. Peer evaluation is a regular task of any leader. It is not set up to punish a person who did not contribute equitably in hindsight. It 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.
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.

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 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.

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.

Weekly schedule

WK	Topic	Learning activity
Pre-semester	Students should complete the online preparation course before the first class. This is available via Canvas. Please note that this material will not be covered in classes and will be assumed knowledge.	Online class (10 hr)
Week 01	2 February 2022 Introduction & Topic 1: Macroeconomic variables and data collection / cleaning	Seminar (4 hr)
Week 02	9 February 2022 Topic 2: Macroeconomic linkages, visualization and data presentation	Seminar (4 hr)
Week 03	16 February 2022 Topic 3: Understanding and modelling demand functions and descriptive analysis (What has happened)	Seminar (4 hr)
Week 04	23 February 2022 Topic 4: Demand modelling exercise and Price/Inflation analysis	Seminar (4 hr)
Week 05	2 March 2022 Topic 5: Unit cost modelling and predictive analysis (What will happen in the future)	Seminar (4 hr)
Week 06	9 March 2022 Topic 6: Operational levers and profit	Seminar (4 hr)
Week 07	16 March 2022 Topic 7: Breakeven Modelling, Optimal Prices, Responding to Higher Unit Costs	Seminar (4 hr)
Week 08	30 March 2022 Topic 8: Risk Exposure Quantification, Natural Hedges, Optimization, Simulation and Forecasting (What should we do)	Seminar (4 hr)
Week 09	13 April 2022 Topic 9: Performance measurement and benchmarking analysis & Revision.	Seminar (4 hr)
Week 10	27 April 2022 Final class: presentations; and exam preparation.	Seminar (4 hr)

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 80% 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. This may involve up to 10 hours of online study depending on your familiarity with Excel.

While we don’t prescribe a textbook, the following provides a nice background reading: Prince, J.T. (2019). Predictive Analytics for Business Strategy: Reasoning from Data to Actionable Knowledge, International Student ed., McGraw-Hill Education (the US edition is 2018)

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. demonstrate and identify (1) the data a business needs to solve complex problems, (2) the key drivers of the demand for a business' product, (3) whether profitability increases in response to an increase in price or output and how this relates to the price elasticity of demand, (4) the key interrelationships between external and internal variables that are pivotal to understanding the business' risk and the management of those risks
LO2. quantify the impact of critical drivers that are external to the company, such as GDP, exchange rates, commodity prices and inflation, on revenue and cost streams and use this information for risk management purposes by utilising key statistical tools such as descriptive statistics, regression and simulation analysis
LO3. communicate complex ideas more effectively to senior management, determine the communication styles that senior management prefer, draw more effective graphical relationships between variables, construct a more effective powerpoint presentation, deliver a presentation that is more likely to be preferred by senior management and construct the most effective sequence of material in a presentation to generate the maximum amount of positive impression from senior management
LO4. collaborate more effectively with colleagues and peers to solve complex data-related problems.

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.

An online preparatory course was created for students with less experience with data analytics. Additional simulation exercises have also been created.

Additional information

Students are encouraged to bring a laptop with Excel to class.

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.

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

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

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

SMBA6003: Data Analytics and Modelling

MBA Session 1, 2022 [Normal evening] - Castlereagh St, 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

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

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