Outline

Overview

The transformation of medicine and health by big data and artificial intelligence is already underway, with ever more routine data collection and its linkage through electronic means. Herein lies the potential to supply real-time personalised healthcare, deep clinical phenotyping and diagnostic capabilities, and prognostic predictions of disease and intervention outcomes. Data science techniques underpin these approaches. This unit will provide a deep dive into understanding the entire end-to-end data cycle / pipeline of healthcare data: from its acquisition (e.g., health records, imaging, sensors etc), to its processing (e.g., cleaning, feature extraction, data linkage etc), to analysing the data (e.g., decision support / computer aided diagnosis) and finally to use the data for prediction (e.g., prognosis and modelling). We will also study the importance of using the data to its stakeholders (patients, clinicians, society etc.) by taking into account of the ethics, privacy, security and measurable benefits from the use of the data. On completion of this unit, students will have a solid understanding of how the healthcare data is now being exploited, through data science principles and tools, to provide improved healthcare delivery. Students will also learn practical skills in healthcare data analysis using Python programming language.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	HTIN4006
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jinman Kim, jinman.kim@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
Written exam	Final Exam Written and closed-book exam	55%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written work	Report on Healthcare Data Science Written report. This will be group-based, with individual components. As a group, students will select a healthcare public data of common interest. Each student will propose, implement, and then discuss, a different hypothesis /analysis using the data.	10%	Week 05 Due date: 27 Mar 2026 at 23:59 Closing date: 27 Mar 2026	max 12 pages (3 pages per student)	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Mid-term Quiz Closed-book Quiz	5%	Week 07 Due date: 15 Apr 2026 at 19:00 Closing date: 15 Apr 2026	30 minutes	AI prohibited
Outcomes assessed:
Written work	Report on Healthcare Data Science – Solution Proposal Written report for feedback. As a group, students will select a healthcare public data of common interest. Each student will propose, implement, and then discuss, a different hypothesis /analysis using the data.	0%	Week 08 Due date: 24 Apr 2026 at 23:59 Closing date: 01 May 2026	upto 10 pages per student (group)	AI allowed
Outcomes assessed:
Interactive oral	Presentation Oral presentation	15%	Week 12 Due date: 20 May 2026 at 17:00 Closing date: 27 May 2026	30 minutes (group)	AI limited - refer to Canvas
Outcomes assessed:
Data analysis	Report on Healthcare Data Science - Data Analysis Written report. This will be group-based, with individual components. As a group, students will select a healthcare public data of common interest. Each student will propose, implement, and then discuss, a different hypothesis /analysis using the data.	15%	Week 12 Due date: 20 May 2026 at 23:59 Closing date: 20 May 2026	10 pages per student	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Fail:
It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
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	Lecture: Introduction to Data Science in Healthcare: Benefits, Challenges and Opportunities	Lecture (2 hr)
Week 01	Tutorial/Lab: Emergence of data-driven AI and automation in healthcare (discussion on recent publications / news). Lab introduction. Setting up Data Science Coding Environment.	Tutorial (1 hr)
Week 02	Lecture: Working with healthcare data – Stakeholder engagement, Ethics, Privacy, Security and Equality	Lecture (2 hr)
Week 02	Lab: Introduction to R – Basics 1	Tutorial (1 hr)
Week 03	Lecture: Understanding Healthcare ‘big’ data sources and basic analytics	Lecture (2 hr)
Week 03	Tutorial/Lab: Data Structure and Visualisation	Tutorial (1 hr)
Week 04	Lecture: End-to-end with Healthcare data: Part I: Data lifecycle, Acquisition, Cleaning and Storing Data	Lecture (2 hr)
Week 04	Lab: Data Manupulations	Tutorial (1 hr)
Week 05	Lecture: End-to-end with Healthcare data: Part II: Querying and summarising data	Lecture (2 hr)
Week 05	Lab: Data Querying and Statistics basics	Tutorial (1 hr)
Week 06	Lecture: End-to-end with Healthcare data: Part III: Hypothesis testing and evaluation	Lecture (2 hr)
Week 06	Lab4: Advanced Data Statistics	Tutorial (1 hr)
Week 07	Lecture: Major types of healthcare data analytics I: Association rules, dimensionality reduction and data clustering (Data Mining)	Lecture (2 hr)
Week 07	Lab5: Machine Learning 1: Fundamentals	Tutorial (1 hr)
Week 08	Lecture: Major types of healthcare data analytics II: Regression Machine Learning	Lecture (2 hr)
Week 08	Lab: Machine Learning 2: Clustering	Tutorial (1 hr)
Week 09	Lecture: Major types of healthcare data analytics III: Classification and Deep Learning	Lecture (2 hr)
Week 09	Lab: Machine Learning 3: Regression	Tutorial (1 hr)
Week 10	Lecture: Dealing with unstructured healthcare data, uncertainty in the data and the predictive models	Lecture (2 hr)
Week 10	Lab: Machine Learning 4: Classification	Tutorial (1 hr)
Week 11	Lecture: Applications and Practical Systems of healthcare data science (Guest Lecturer)	Lecture (2 hr)
Week 11	Open Lab for Assessment	Tutorial (1 hr)
Week 12	Lecture: Healthcare data science research, tools, datasets, and the community	Lecture (2 hr)
Week 12	Healthcare data science presentation	Tutorial (1 hr)
Week 13	Lecture: Unit of study review	Lecture (2 hr)
Week 13	Healthcare data science presentation	Tutorial (1 hr)

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

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. Identify contemporary healthcare challenges and how novel artificial intelligence-based solutions can address them.
LO2. Identify and understand the sources of healthcare data, and how they together pertain to health.
LO3. Understand the full pipeline of health data generation, collation, processing, analytics and predictive modelling, and presentation to healthcare stakeholders so as to support decision making.
LO4. Understand ethical and security best practices as they apply to the use of healthcare data.
LO5. Create and execute machine learning-based models around a real health dataset, visualise, interpret and present the results.
LO6. Communicate effectively across data science and healthcare disciplines in understanding a healthcare challenge, devising a data science solution and interpreting its results.

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.

Updating the lecture content to focus deeper into fewer examples / case studies. Simplifying the lab contents

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

HTIN5006: Foundations of Healthcare Data Science

Semester 1, 2026 [Normal evening] - 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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links