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Unit outline_

HTIN5006: Foundations of Healthcare Data Science

Semester 1, 2023 [Normal evening] - Remote

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
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Jinman Kim,
Type Description Weight Due Length
Supervised exam
Final Exam
Written and closed-book exam
55% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Individual report on Healthcare Data Science
Written report
10% Week 05
Due date: 24 Mar 2023 at 23:59
max 10 pages
Outcomes assessed: LO1 LO2 LO6
Online task Mid-term Quiz
Closed-book Quiz
5% Week 06 30 minutes
Outcomes assessed: LO1 LO4 LO3 LO2
Assignment Individual report on Healthcare Data Science – Solution Proposal
Written report
10% Week 08
Due date: 13 Feb 2023 at 10:21
5 pages
Outcomes assessed: LO3 LO4 LO5 LO6
Presentation Presentation
Oral presentation
10% Week 12
Due date: 19 May 2023 at 23:59
30 minutes
Outcomes assessed: LO1 LO6 LO4 LO3 LO2
Assignment Individual report on Healthcare Data Science - Data Analysis
Written report
10% Week 12
Due date: 19 May 2023 at 23:59
10 pages
Outcomes assessed: LO3 LO5 LO6

Assessment summary

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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 Lecture: Introduction to Data Science in Healthcare: Benefits, Challenges and Opportunities Lecture (2 hr) LO1
Tutorial1: Emergence of data-driven AI and automation in healthcare (discussion on recent publications / news) Tutorial (1 hr) LO1
Week 02 Lecture: Understanding Healthcare ‘big’ data sources and basic analytics Lecture (2 hr) LO2
Tutorial2: Data flow within a hospital / local health district / national infrastructure Tutorial (1 hr) LO2
Week 03 Lecture: Working with healthcare data – Stakeholder engagement, Ethics, Privacy, Security and Equality Lecture (2 hr) LO4
Lab1: Introduction to R – Basics 1 Computer laboratory (1 hr) LO5
Week 04 Lecture: End-to-end with Healthcare data: Part I: Data lifecycle, Acquisition, Cleaning and Storing Data Lecture (2 hr) LO3
Lab2: Introduction to R – Basics 2 Computer laboratory (1 hr) LO5
Week 05 Lecture: End-to-end with Healthcare data: Part II: Querying and summarising data Lecture (2 hr) LO3
Lab3: Summarising Data Computer laboratory (1 hr) LO5
Week 06 Lecture: End-to-end with Healthcare data: Part III: Hypothesis testing and evaluation Lecture (2 hr) LO3
Lab4: Plotting Data Computer laboratory (1 hr) LO5
Week 07 Lecture: Major types of healthcare data analytics I: Association rules, dimensionality reduction and data clustering (Data Mining) Lecture (2 hr) LO5
Lab5: Basic statistical analysis Computer laboratory (1 hr) LO5
Week 08 Lecture: Major types of healthcare data analytics II: Regression and Classification (Machine Learning) Lecture (2 hr) LO5
Lab6: Linear Regression Computer laboratory (1 hr) LO5
Week 09 Lecture: Dealing with unstructured healthcare data, uncertainty in the data and the predictive models Lecture (2 hr) LO5
Tutorial3: Understanding the role of data in healthcare application Tutorial (1 hr) LO3 LO4 LO5
Week 10 Lecture: Applications and Practical Systems of healthcare data science I Lecture (2 hr) LO2 LO3 LO4
Tutorial4: Implementation of AI tools in healthcare Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 11 Lecture: Applications and Practical Systems of healthcare data science II Lecture (2 hr) LO2 LO3 LO4
Open Lab for Assessment Computer laboratory (1 hr) LO5
Week 12 Lecture: Healthcare data science research, tools, datasets, and the community Lecture (2 hr) LO1 LO2 LO3 LO6
Open Lab for Assessment Computer laboratory (1 hr) LO5
Week 13 Lecture: Unit of study review Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Healthcare data science presentation Presentation (1 hr) LO6

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. 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

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



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.