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Unit outline_

FMBA5008: Data Analytics and Modelling

Semester 1b, 2023 [Block mode] - Castlereagh St, Sydney

This unit aims to develop the ability to effectively analyse and draw useful inferences from data in order to communicate complex interrelationships to senior management in a way that can lead to favourable and sustainable change. Topics covered include analysis of key macroeconomic variables and statistical concepts, demand modelling, performance measurement and benchmarking analysis, and quantifying the economics and financial risks. This unit provides students with an opportunity to work with real-world data sets and case studies, and to apply those data sets to various organisations

Unit details and rules

Academic unit Management Education
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
SMBA6003
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator John-Paul Monck, john-paul.monck@sydney.edu.au
Type Description Weight Due Length
Assignment Individual assignment
Report
40% Week 05
Due date: 14 May 2023 at 11:59
6 pages
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment group assignment Group assignment
Presentation
40% Week 06
Due date: 18 May 2023 at 23:59
3 slides per member
Outcomes assessed: LO1 LO2 LO3 LO4
Supervised exam
? 
Final exam
Short answer and MCQ
20% Week 07
Due date: 25 May 2023 at 10:00
2.5 hours
Outcomes assessed: LO2 LO3 LO4
group assignment = group assignment ?

Assessment summary

  • Individual assignment including sustainability component: Students will write a professional report explaining how data analysis can be used to address a long-term issue of sustainable development within the Australian economy or another economy of your choice.
  • Group assignment: In groups of 5-6, students will prepare a powerpoint presentation discussing the key demand side risks associated with the venture with a focus on the macroeconomic performance of the target country.
  • Final exam: The final exam will cover all the topics of the unit.

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 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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Basic data analytic skills for students without a strong background in this area Lecture and tutorial (4 hr)  
Macroeconomic overview Lecture and tutorial (4 hr)  
Week 02 Macroeconomic linkages Lecture and tutorial (4 hr)  
Demand modelling Lecture and tutorial (4 hr)  
Week 04 Demand modelling exercise Lecture and tutorial (4 hr)  
Cost modelling Lecture and tutorial (4 hr)  
Week 05 Microeconomic levers Lecture and tutorial (4 hr)  
Break even analysis Lecture and tutorial (4 hr)  
Week 06 Economic risks Lecture and tutorial (4 hr)  
Performance measurement and benchmarking Lecture and tutorial (4 hr)  

Attendance and class requirements

Lecture recordings and attendance: 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).

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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

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.