Outline

Overview

Recently, we observed a rise in healthcare and civic disasters, including but not limited to the COVID19 pandemic, opioid crisis, bush fires, floods, droughts, etc. These events have significant impact not only on the way people live and work, but also seriously affect business organization, strategy, and management. With the rapidly increasing availability of data on health and civic disasters, it becomes increasingly important to learn how we can utilize available information and data sources in order to predict, alleviate and adjust to their impact. This unit of study provides a comprehensive introduction into methods and tools, which could be utilized to better understand and manage the effects of major health, climate and other civic events by analysing and deriving important intelligence from data.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	No prior knowledge is assumed although some familiarity with mathematical and statistical concepts will be an advantage
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Adam Dunn, adam.dunn@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Research analysis	Final Short Release Assignment Critical evaluation of papers and Implementation of methods	50%	Formal exam period Due date: 11 Jun 2026 at 13:00	2 days	AI allowed
Outcomes assessed:
Written test	In-semester test Written test	35%	Week 08 Due date: 22 Apr 2026 at 13:20	1.5 hours	AI prohibited
Outcomes assessed:
Contribution	Weekly participation assignments n/a	15%	Weekly	Varied	AI allowed
Outcomes assessed:

Assessment summary

Weekly participation assignments: 15%

Mid-term exam: 35%

Assignment: 50%

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.

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.

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 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

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Health and Civic Analytics	Lecture (2 hr)
Week 01	Introduction to Health and Civic Analytics	Tutorial (1 hr)
Week 02	Data Types, Data Sources, Data Access and Data Context	Lecture (2 hr)
Week 02	Data Types, Data Sources, Data Access and Data Context	Tutorial (1 hr)
Week 03	Health Data Life Cycle	Lecture (2 hr)
Week 03	Health Data Life Cycle	Tutorial (1 hr)
Week 04	Epidemic Modelling	Lecture (2 hr)
Week 04	Epidemic Modelling	Tutorial (1 hr)
Week 05	Clinical Decision Support Systems	Lecture (2 hr)
Week 05	Clinical Decision Support Systems	Tutorial (1 hr)
Week 06	Clinical Trials	Lecture (2 hr)
Week 06	Clinical Trials	Tutorial (1 hr)
Week 07	Clinical Epidemiology and Systematic Reviews	Lecture (2 hr)
Week 07	Clinical Epidemiology and Systematic Reviews	Tutorial (1 hr)
Week 08	Precision and Personalised Medicine	Lecture (2 hr)
Week 08	Precision and Personalised Medicine	Tutorial (1 hr)
Week 09	Implementing and Managing Digital Health Tools	Lecture (2 hr)
Week 09	Implementing and Managing Digital Health Tools	Tutorial (1 hr)
Week 10	Causal Inference Methods	Lecture (2 hr)
Week 10	Causal Inference Methods	Tutorial (1 hr)
Week 11	Civic Event Management Using Data	Lecture (2 hr)
Week 11	Civic Event Management Using Data	Tutorial (1 hr)
Week 12	Advanced AI tools	Lecture (2 hr)
Week 12	Advanced AI tools	Tutorial (1 hr)
Week 13	Revision	Lecture (2 hr)
Week 13	Revision	Tutorial (1 hr)

Attendance and class requirements

Students are required to both attend class and participate in tutorials

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

There is no required text for this unit.

A recommended text for the course is given below:

Dicker et al (2006), Principles of Epidemiology in Public Health Practice, CDC, Third Edition, An Introduction to Applied Epidemiology and Biostatistics, https://www.cdc.gov/csels/dsepd/ss1978/SS1978.pdf

Articles and other relevant literature for the unit will be provided.

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: Ability to understand how to define a real-world problem related to healthcare and civic issues
LO2. Measure: Ability to identify appropriate measures, variables, and datasets as well as understand how to collect suitable data
LO3. Analyze: Ability to select the most appropriate methods and techniques to analyse the data and solve the problem
LO4. Critically evaluate: Ability to critically evaluate existing data science methodology suitable for healthcare and civic applications
LO5. Communicate: Ability to communicate health and civic analytics findings for business-level understanding
LO6. Be ethical and responsible: Ability to approach all stages of the health and civic problem-solving process in a 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.

Student feedback was very positive and was utilized to give further considerations to emphasize statistical foundations which are required as part of the course.

Additional information

Business School Policies

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

QBUS5015: Foundations of Health and Civic Analytics

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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