UNSW Medicine
Postgraduate Programs in
Health Data Science
UNSW Sydney is
ranked 43
th
in the 2020
QS World Universities
Rankings
43
rd
Attracting the
brightest students who
can learn alongside
world leading
researchers
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AND
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PROFESSIONALS
Top World Ranking
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Flexible Professional Training
Opportunities for
research and
internship in industry
and government
AND
CONTRIBUTING
TO HEALTH
WELFARE
Delivered by Health
Big Data Experts
UNSW is a member of
the prestigious Group
of Eight – a coalition
of Australia’s leading
research intensive
universities
Leader in Education
Go8Go8
Welcome from the Dean
Welcome from the Director
Healthcare needs Health Data Scientists
Why experts are calling data science the sexiest job in the world
Health Data Science Overview
Faculty
Health Data Science Programs
Health Data Science Program Courses
Entry Requirements
Admission and Enrolment Process
Indicative Fees and Key Dates
4
5
6-7
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9
10
11
12-15
16
17
18-19
Contents
4
At UNSW Sydney, we work closely
with some of Australia’s nest
hospitals, research institutes and
healthcare organisations. These
partnerships and our collective
capacity allow us to continuously
improve the way medicine is learned
and how it is practiced. We are
constantly evolving, analysing the
way modern healthcare is delivered
and capitalising on new health
technologies and insights to integrate
them into our courses, research and
clinical environments.
At the Centre for Big Data Research
in Health, we welcome you to UNSW
Medicine’s global community. As a
graduate, you will contribute to
extracting crucial knowledge and
insights from health big data to inform
clinical care and health policy
decisions. We are committed to
helping you develop the knowledge,
skills and values necessary to make a
difference.
Scientia Professor Vlado Perkovic
Dean, UNSW Medicine
UNSW Sydney
Welcome to UNSW Medicine. As one of the world’s
top medical schools, our aim is to support and
develop our students, our clinicians and our
researchers to be leaders and innovators in health.
We attract brilliant students from across the globe
who collaborate with our world-leading researchers
and clinicians to translate knowledge into
breakthrough treatments and cures that directly
benefit our communities.
Welcome from the Dean
Welcome to the Health Data Science prospectus,
a guide to the programs on offer at UNSW
Medicine with the Centre for Big Data Research
in Health – a world-first centre dedicated to
health research using big data.
The Master of Science, Graduate
Diploma and Graduate Certicate in
Health Data Science are the rst
Australasian postgraduate programs
in Health Data Science – an exciting
discipline that spans the domain
areas of health and medicine,
mathematics, statistics and computer
science. The Health Data Science
programs aim to equip graduates with
the essential cognitive, analytical and
communication skills needed to make
sense of health big data.
“Big data” have no agreed denition,
but the term is generally applied to
data that by virtue of their size and/or
complexity pose challenges to
traditional methods for management
and analysis. In health, such data
include the millions of records that
are generated routinely by health
services, real-time clinical data
captured at the point-of-care,
genomic and imaging data produced
in research and clinical settings, and
health-related data generated by the
population at large through
technologies such as wearable
devices and social media. How we
make use of this data and transform it
into action to better support clinical
care, inform health policy and
improve population health has never
been more important than now.
We sit at a key point in history, where
big data are poised to become a
dominant driver in what happens in
health and healthcare. We have
designed our programs in response
to a signicant and growing gap in the
global health workforce: skilled data
scientists who understand the context
of health and can apply data analytics
to drive health improvement.
In these programs, students are
exposed to real-world problems and
are taught the latest analytical
methodologies to derive solutions.
A major feature of the learning
experience is a novel social online
community giving students
opportunities to interact with peers
and instructors from diverse
backgrounds and workplaces.
The Health Data Science programs
will provide pathways into a wide
range of health analytics-based
careers, or for existing professionals
will allow you to understand health
big data and use it in your work.
This is an exciting time to become a
Health Data Scientist, we welcome
you to join us on the journey.
Professor Louisa Jorm
Director, Centre for Big Data
Research in Health
Health Data Science
5
Welcome from the Director
6
UNSW Sydney
Healthcare needs Health Data Scientists
THE DATA DELUGE
33 TRILLION GB
The total amount of data produced in human history up to the year 2018
#1
Where career experts Glassdoor.com ranked data scientist in their
US job rankings for 2016, 2017, 2018 and 2019, based on job
satisfaction, number of job openings and salary
75 DAYS
How long it takes for global health data to double in size
75
172.2 MILLION
The number of wearable tness trackers sold worldwide in 2018
1.7 MILLION
The estimated shortfall in the number of data analysts required in the US in 2018
175 TRILLION GB
The total amount of data humans will have produced by 2025. If that
much info was contained in a string of USB each storing 128GB, it
would reach the moon and back almost 100 times.
HEALTH DATA SCIENTISTS IN DEMAND
7
Health Data Science
2.5 X
The rate Indigenous Australians are admitted to hospitals compared to
non-Indigenous Australians - a problem Health Data Scientists at UNSW
are working to address
Top 25
LinkedIn’s Most In-Demand Hard Skills 2019 ranking of Analytical
Reasoning, Scientic Computing and Data Science
204 MILLION
The number of prescriptions issued in Australia in 2017-2018
11.3 MILLION
The number of recorded hospitalisations in Australia in 2017-2018
$130,000
The median annual salary for analytics professionals in Australia
1 YEAR
How long it can take for infectious disease researchers to gain
access to death certicates, which seriously hinders our ability to
respond to new epidemics
NEED FOR BIG DATA ANALYTICS IN HEALTH
Why experts are calling data
science the sexiest job in the world
It’s not often that an occupation is
crowned “sexiest job of the century” only
a few years after it pops up. But data
scientists have bucked the trend and are
reigning supreme in the job market –
and they’re reaping some impressive
benets as a result.
Procient at navigating vast oceans of
information to sh out hidden trends and
patterns, data scientists are at the
forefront of computer science and
articial intelligence. They develop
software and algorithms to target the
information they’re seeking and then,
from that mind-numbingly massive
coalface of big data, they extract
invaluable insights for their clients.
Now, less than a decade since LinkedIn
and Facebook analysts popularised the
term “data scientist”, it’s become one of
the most coveted roles for ambitious,
tech-savvy individuals, with some of the
world’s most successful companies like
Google and Walmart scrambling to snap
them up.
And that’s why back in 2012, the
esteemed Harvard Business Review
bestowed the title of “Sexiest job of the
21st century” on data scientists.
“Think of him or her as a hybrid of data
hacker, analyst, communicator, and
trusted adviser,” write The Review’s
Thomas H. Davenport and D.J. Patil.
“The combination is extremely powerful
– and rare.”
Leading a healthcare revolution
In the healthcare sector, data scientists
are now coming into their own -
particularly with the recent opening of
UNSW’s world-leading Centre for Big
Data Research in Health.
Using large-scale electronic data that
spans the biomedical, clinical and
health services domains, the Centre
brings together more than 60 research
staff and students to tackle critical
health issues facing Australian and
global communities.
“I love it because it’s so stimulating,”
says Professor Sallie-Anne Pearson,
head of the Centre’s Medicines Policy
Research Unit, which uses big data to
determine how medicines are being
used locally and internationally.
“It’s science, but there’s a lot of art in
this.”
Data scientists in the health sector can’t
rely on their technical skill alone, says
Peter Cronin, co-founder and managing
director of Prospection, a Sydney-
based health insights company. They
also need people skills, communication
skills, and an inquiring mind.
“Being a good data scientist in this
sector means rst being able to
understand what is the question that
someone wants to ask. Then you need
to understand the data you’re working
with, including the limitations of that
data,” Cronin explains.
Big data scientists are entering the world of
healthcare, and the impact is set to be revolutionary.
8
UNSW Sydney
“Thirdly, you have to be able to develop
algorithms. And the fourth component
is knowing how to communicate or
visualise the answer to be able to
present it to the client.”
“Within healthcare there are a lot of
nuances to the data, and healthcare
can be complex,” he says. “It really
helps to understand disease and that
sort of thing.”
Centre researcher Associate Professor
Georgina Chambers of the National
Perinatal Epidemiology and Statistics
Unit says the exponentially growing
area of big data in health is enthralling,
but it’s in desperate need of more data
scientists to grapple with the issues
that come from accessing such data.
“I’m excited by it, but what I sense is
that, like a lot of medical science, the
science is going to move a lot faster
than individuals’ and society’s ability to
digest it,” she says.
And because data science is cross-
disciplinary, Pearson adds, there are
opportunities for everyone.
“What I love about this eld is it’s so
diverse,” she says. “I’m a behavioural
scientist – I’m not a doctor, I’m not a
pharmacist. But I’ve been able to
establish a research career. Data
science welcomes everyone.”
9
Health Data Science
Health Data Science is the science and art of
generating data-driven solutions through
comprehension of complex real-world health
problems, employing critical thinking and
analytics to derive knowledge from (big) data.
The Health Data Science programs are
designed for those new to Health Data
Science and those already working in
the eld looking to up-skill. Whether
you are a statistician who wants to
build on your current skills with
exposure to a eld where you can
make an impact; a clinician or nurse
who wants to expand your capabilities
and improve the quality of care
received by your patients; or a keen
programmer looking to convert your
“on-the-job” experience into a formal
qualication, our programs welcome
students from a wide range of
backgrounds.
Students are guided through the Health
Data Science pipeline from the context
of health and data curation through to
analytics, computation and
communication. Our programs use
real-world examples and innovative
teaching techniques to ensure students
gain the essential skills for a career in
Health Data Science.
Graduates of the Master of Science
program can function at any stage
along the Health Data Science pipeline
including designing and leading
research studies or evaluations,
conducting complex analyses,
managing teams of data analysts or
acting as health policy advisors on the
outcomes of study ndings. The Health
Data Scientists arising from these
programs will have a breadth of skills
for many different roles in the arena of
health big data. Employers of Health
Data Scientists can include government
Departments of Health, hospitals,
universities and research institutes,
pharmaceutical companies, health
insurance companies and private data
analytics consultancies.
As part of the Centre for Big Data
Research in Health at UNSW, you’ll
belong to a vibrant community of
researchers, educators, and students.
Our team have world-leading expertise
in managing, manipulating, analysing
and visualising health big data. We
welcome you to become part of it.
Andrew Blance
Program Director Health Data
Science, Centre for Big Data
Research in Health
Health Data Science Overview
Faculty
Professor Louisa Jorm (Centre for Big
Data Research in Health, UNSW)
Professor Georgina Chambers (Centre
for Big Data Research in Health,
UNSW)
Professor Sallie Pearson (Centre for
Big Data Research in Health, UNSW)
Professor Claire Vajdic (Centre for Big
Data Research in Health, UNSW)
Andrew Blance (Centre for Big Data
Research in Health, UNSW)
Sanja Lujic (Centre for Big Data
Research in Health, UNSW)
Dr Timothy Churches (Ingham Institute
for Applied Medical Research and
South Western Sydney Clinical
School, UNSW)
Dr Oscar Perez Concha (Centre for
Big Data Research in Health, UNSW)
10
UNSW Sydney
Dr James Farrow (Centre for Big
Data Research in Health, UNSW)
Dr Amy Gibson (Centre for Big Data
Research in Health, UNSW)
Dr Mark Hanly (Centre for Big Data
Research in Health, UNSW)
Dr Sebastiano Barbieri (Centre for
Big Data Research in Health, UNSW)
Dr Stephanie Choi (Centre for Big
Data Research in Health, UNSW)
Dr Natasha Donnolley (Centre for Big
Data Research in Health, UNSW)
Dr Michael Falster (Centre for Big
Data Research in Health, UNSW)
Dr Alys Havard (Centre for Big Data
Research in Health, UNSW)
Dr Kylie-Ann Mallitt (Centre for Big
Data Research in Health, UNSW)
Dr Andrea Schaffer (Centre for Big
Data Research in Health, UNSW)
Dr Duong Tran (Centre for Big Data
Research in Health, UNSW)
Dr Marina van Leeuwen (Centre for
Big Data Research in Health, UNSW)
Dr Jonathan Brett (St Vincent’s
Hospital and Centre for Big Data
Research in Health, UNSW)
Dr Bronwyn Brew Haasdyk (Centre for
Big Data Research in Health, UNSW)
Dr Benjamin Daniels (Centre for Big
Data Research in Health, UNSW)
Dr Helga Zoega (Centre for Big Data
Research in Health, UNSW)
Dr Juliana de Oliveira Costa (Centre for
Big Data Research in Health, UNSW)
Health Data Science Pipeline
Context
Curation
Analytics
Computation
Communication
Health Delivery
Systems
Data Sources
Evidence-Based
Medicine
Health Outcomes
Health Equity and
Genes
Data Quality
Wrangling
Data Linkage
File Management/
Storage
IT Security
Pattern Analysis
Pre-Processing
Outlier Detection
Prediction
Algorithms
Models
Decision Making
Collaborative
Coding
Visualisation
Written and Oral
Presentation
Health Data Science
Health Data Science Programs
The Master of Science in Health Data
Science is fully articulated with
options for a Graduate Certicate and
Graduate Diploma degree. Students
may exit the Master of Science with a
Graduate Certicate or Graduate
Diploma if they meet the
requirements of these programs.
The entry requirements for these
programs are described on page 16.
Students progress through the Health
Data Science pipeline as they work
through the degree levels. In the
Graduate Certicate, students
acquire foundational skills in health
context, programming, statistics and
data management that progresses
into skills in advanced statistics,
machine learning and data
visualisation in the Graduate
Diploma. The Master of Science
provides an opportunity to experience
the entire Health Data Science
pipeline using a real-world health
data problem through a dissertation
or workplace internship, or students
can select a capstone project and
further their technical learning with a
choice of electives. High achieving
graduates of the Master of Science
will have potential for consideration of
PhD enrolment.
The programs are available as
full-time or part-time, and as
on-campus or fully online mode.
Program
Units of
Credit
Duration Study Mode Courses
Graduate
Certicate 7372
(CRICOS 096227J)
24
(4 core
courses)
0.7 year
full-time
Full-time or
part-time;
on-campus or
fully online
• HDAT9100 Context of Health Data Science
• HDAT9200 Statistical Foundations for Health
Data Science
• HDAT9300 Computing for Health Data Science
OR COMP9021 Principles of Programming
• HDAT9400 Management and Curation of
Health Data
Graduate
Diploma 5372
(CRICOS 096226K)
48
(8 core
courses)
1 year
full-time
Full-time or
part-time;
on-campus or
fully online
• HDAT9500 Health Data Analytics:
Machine Learning and Data Mining
• HDAT9600 Health Data Analytics:
Statistical Modelling I
• HDAT9700 Health Data Analytics:
Statistical Modelling II
• HDAT9800 Health Data Analytics:
Visualisation and Communication of Health Data
Master of
Science 9372
(CRICOS 096225M)
72
(8 core
courses +
Research OR
Research
Capstone and
Electives
1.7 years
full-time
Full-time or
part-time;
on-campus or
fully online
• HDAT9900-9902 Health Data Science: Dissertation
OR
• HDAT9910 Health Data Science: Capstone
• Choice of Electives up to 18 UOC
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12
UNSW Sydney
Health Data Science Program Courses
Teaching Approach
Our Health Data Science courses use a blended
education teaching approach for a student-centric
learning experience. In some courses, a ipped
classroom approach is utilised wherein students learn
theory from short online videos and then use face-to-
face or online sessions to apply knowledge, practice
skills or for peer-to-peer learning. Varied assessment
modalities are also used throughout the program to
engage critical thinking, independent reection or team
work skills, for example reective blogs, multiple choice
question games and collaborative coding group projects.
HDAT9100 Context of Health Data Science
6 UOC
The Context of Health Data Science provides an introduction
to how data are generated and used in a contemporary
health system. We look at how health outcomes can be
measured and reported in various forms of health data, and
how these health data can reveal inequalities in health. The
course describes the major sources of health data, including
those relating to primary care, hospital stays and prescription
medicines, and how this (and other) information can be used
by the health data scientist to create evidence for policy and
research.
Activities are structured to foster a scientic, questioning
attitude in the student. Students are encouraged to think
critically about how health data are recorded, what this
reveals about the underlying health delivery systems, and be
creative in their use of health data sources to create or
critically appraise evidence.
HDAT9200 Statistical Foundations
for Health Data Science
6 UOC
Health data is often complex and noisy. Obtaining actionable
insights (or revealing the hidden signals) from such data requires
the utilisation of probabilistic concepts. Thus a solid understanding
of the principles of statistics is intrinsic to Health Data Science.
The aim of this rst course in probability theory is to introduce the
foundations required to understand such phenomena.
The course design is highly innovative and novel. Statistical
computing is used to gain a sound understanding of statistical
theories and concepts. Specically, this course draws on the
practical application of Monte Carlo algorithms, which are a
very effective method of statistical computing. Once this
illustrative approach has (a posteriori) demonstrated a theory, it
will then be stated formally.
HDAT9300 Computing for Health Data
Science 6 UOC
Computing now pervades nearly every aspect of modern life,
including health care delivery and health services management.
The objective of this course is to develop ‘computational thinking’
in health data science students, by providing you with a thorough
and principled introduction to computer programming, algorithms,
data structures and software engineering best practices. The
ability to write clear, efcient and correct computer code is at the
core of most data science practice and is a foundation skill set.
In this course, you will learn to program in the Python language
through tackling health-related problems. Topics include data
types, functions, data processing, simulation, software
development and program testing and debugging. Theoretical
principles are reinforced with extensive ‘hands-on’ coding in
Python, including the NumPy and Pandas packages.
Core material will be delivered as short lectures and readings
supported by interactive coding activities. Practical exercises
will utilise Spyder/Jupyter Notebook documents.
13
Health Data Science
HDAT9400 Management and Curation
of Health Data
6 UOC
This course is designed to equip students with the skills
required to collect or obtain data, design data
management strategies aligned with best practice, and
appreciate the day to day practicalities of data curation for
sound data management. Students will develop data
wrangling skills required to assemble data suitable for
analysis and research purposes, including data from
linkage projects. Data wrangling skills will focus on the
key areas of data security, data exploration,
documentation of data and data management, with the
ultimate aim of creating analysis-ready datasets and
ensuring reproducible results.
HDAT9500 Health Data Analytics:
Machine Learning I
6 UOC
Healthcare organisations have a vast amount of data:
electronic medical records, claims, registries, medical
images, and other types of digital health data. Machine
learning techniques learn from previous experience in order
to discover patterns and relationships in data, and have
been found to perform extremely well in large datasets.
This course provides an introduction to machine learning
techniques through a series of health applications.
Algorithms for supervised and unsupervised learning are
covered, including linear regression and classication,
tree-based methods, clustering, dimensionality reduction and
neural networks.
Students will learn about the underlying supporting theory of
these techniques, as well as gain the applied practical skills
required to effectively apply these techniques to new health
data problems.
HDAT9600 Health Data Analytics:
Statistical Modelling I
6 UOC
This course provides a sound grounding in the theory and
practice of tting statistical regression models, with particular
focus on the exibility of generalised linear models (GLMs).
Starting with linear regression, a major theme of the course
is best practice in model tting, including thorough
exploratory data analysis, model assumption checking, data
preparation and transformation, including the use of
imputation, and careful attention to model adequacy and
diagnostics. Emphasis is given to content-aware, purposive
model building and the use of Directed Acyclic Graphs
(DAGs) of causal relations to inform model parameter
selection. Non-linear, logistic, binomial and Poisson models
for count data are also covered. Effect modications
(interactions) and their meaning in a health context are
explored. The presentation and visualisation of statistical
models is considered, with emphasis on the explanatory
insights that can be gained from well-constructed models.
The nal part of the course covers basic time-series models,
survival analysis and other time-to-event models.
HDAT9700 Health Data Analytics:
Statistical Modelling II
6 UOC
Sophisticated modelling techniques are essential for the
analysis of real-world health data. Building on Health Data
Analytics: Statistical Modelling I (HDAT9600), this course
expands the statistical toolkit and broadens students’
understanding of relevant statistical approaches for the
analysis of realistically complex data structures and
research questions. The course is aimed at those
currently working or planning on working in health or a
health-related eld, and who are interested in applying
advanced statistical methods to analyse complex data.
14
UNSW Sydney
Health Data Science Program Courses
HDAT9900-02 Health Data Science:
Dissertation 6, 12 or 18 UOC
The dissertation consists of extensive (directed)
independent research with an academic supervisor. The
learning from the Graduate Diploma scaffolds to this
‘realworld’ project. In addition to developing sound project
management skills, this course facilitates the bigger
picture - the Health Data Science pipeline is experienced
from start to nish.
Support is given via weekly supervisory meetings,
supplemented with additional workshops dependent on
specic project requirements. An additional early
checkpoint involves the development and submission of
study protocol and literature review. The nal outputs will
mirror those of a real world academic setting. Specically
the production of a manuscript to the specications of a
peer reviewed journal relevant to the project and of
publishable standard. The project is also to be
disseminated orally via a 15 minute presentation
(including 5 minutes of questions and answers).
Students are required to complete Graduate Diploma to a
satisfactory standing to be admitted onto this course. The
choice of project could either be selected from an offered
list of projects or developed from students proposals,
dependent on the availability of a suitable supervisor and
agreement on project topic.
Students are required to complete a total of 24 UOC in
Dissertation courses over a number of terms. This can be
completed on a full-time or part-time basis.
Topics covered in this course include multilevel models for
hierarchical data; analysis of time series and longitudinal
data; quasi-experimental approaches for drawing causal
inferences from observational data; multiple imputation for
missing values; and simulation approaches for study
planning and model evaluation.
Content is delivered through a combination of online
readings, expert guest lectures and practical hands-on
tutorials. Statistical concepts are illustrated with a variety
of health examples, and students will learn how to
implement methods using leading statistical software.
Lectures are followed by exercises, which reinforce the
learning and programming skills covered in the face-to-
face tutorials.
HDAT9800 Visualisation and
Communication of Health Data
6 UOC
Health Data Scientists need to interface with audiences
from a broad and varied array of backgrounds, and across
the full technical spectrum from lay through to specialist.
Thus effective communication is an essential attribute.
Further, this capability is required across different media
in the forms of the written word, oral presentation and Vis
(data communication via a visual medium).
This course takes a toolbox approach to creating
appropriate, reproducible and transparent analyses and
visualisations. In the context of R, it presents useful
best-practice data science analysis and visualisation
techniques with a focus on different types of data
visualisations. A basic understanding of how people
process information can ensure communication remains
effective to an audience with a disability.
15
Health Data Science
Electives Up to 18 UOC
• BINF9010 - Applied Bioinformatics (6 UOC)
• BINF9020 - Computational Bioinformatics (6 UOC)
• BIOM9450 - Clinical Information Systems (6 UOC)
• COMP9021 - Principles of Programming (6 UOC)
• COMP4121 - Advanced and Parallel Algorithms (6 UOC)
• COMP6714 - Information Retrieval and Web Search
(6 UOC)
• COMP9024 - Data Structures and Algorithms (6 UOC)
• COMP9101 - Design and Analysis of Algorithms (6 UOC)
• COMP9311 - Database Systems (6 UOC)
• one of the following:
o
COMP9313 - Big Data Management (6 UOC)
o
COMP9318 - Data Warehousing and Data Mining
(6 UOC)
• COMP9319 - Web Data Compression and Search (6 UOC)
• MATH5165 - Optimization (6 UOC)
• MATH5425 - Graph Theory (6 UOC)
• MATH5845 - Time Series (6 UOC)
• MATH5885 - Longitudinal Data Analysis (6 UOC)
• MATH5905 - Statistical Inference (6 UOC)
• MATH5945 - Categorical Data Analysis (6 UOC)
• MATH5960 - Bayesian Inference and Computation
(6 UOC)
• one of the following:
o
PHAR9114 - Health Technology Assessment in Australia
(6 UOC)
o
PHAR9115 - Advanced Health Technology
Assessment (6 UOC)
• PHAR9120 - Clinical Trials (6 UOC)
• PHAR9121 - Pharmacovigilance (6 UOC)
HDAT9910 Research Capstone
6 UOC
The learning from the Graduate Diploma (5372) scaffolds to
this six unit of credit ‘desk-based’ research, capstone project.
The overarching aim is to facilitate the bigger picture of
Health Data Science (HDS); the student experiences the
HDS pipeline from start-to-end. Thus, the student is
presented with the opportunity to bring all the content of the
Graduate Diploma together, realising the relative ordering
and merits of each stage. This capstone has the advantage
of allowing a further 18 units of credit of broadening electives
to be undertaken.
The capstone project involves completing extensive,
desk-based, independent research tasks, requiring the use
of the R and/or Python programming languages. An entire
HDS project has been constructed and sliced into the
respective stages of the HDS pipeline. At each stage, the
student has the option of completing minor or major tasks to
progress to the next stage. For example, at the ‘Curation’
stage, a minor task might be a short-written report (circa
1,000 words) identifying the issues to be addressed. A major
task would involve preparation of a data management plan
(DMP; circa 3,000 words). Each task will be assigned a
point score based on its complexity, proportional to the
expected (notional) time required to complete the task. To
complete the course, will require successful completion of
three minor and two major tasks.
Students are required to complete Graduate Diploma in
Health Data Science (5372) to a satisfactory standing to be
admitted onto this course.
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UNSW Sydney
Domestic and international students from a broad range of
backgrounds including healthcare, mathematics, statistics
and computer science are encouraged to apply.
Direct entry is available for the Master of Science and
Graduate Certicate programs. Entry into the Graduate
Diploma is through the successful completion of the
Graduate Certicate program.
Master of Science in Health Data Science
(Program 9372)
The entry criteria are:
• an undergraduate degree in a cognate discipline or
• an undergraduate degree in a non-cognate discipline at
honours level or
• successful completion of Graduate Diploma in Health Data
Science (Program 5372) or
• qualications equivalent to or higher than Graduate
Diploma in Health Data Science (Program 5372; case-by-
case basis)
Graduate Diploma in Health Data Science
(Program 5372)
The entry criteria:
• an undergraduate degree in a cognate discipline or
• an undergraduate degree in a non-cognate discipline at
honours level or
• successful completion of Graduate Certicate in Health
Data Science (Program 7372) or
• qualications equivalent to or higher than Graduate
Certicate in Health Data Science (Program 7372; case-by-
case basis)
Entry Requirements
Graduate Certificate in Health Data Science
(Program 7372)
The entry criteria are:
• an undergraduate degree in a cognate discipline or
• an undergraduate degree in a non-cognate discipline at
honours level or
• an undergraduate degree in a non-cognate discipline and
minimum 1-year full-time equivalent of relevant work
experience or
• minimum 3-years full-time equivalent of relevant work
experience and tertiary-level training, demonstrating
capability in a cognate discipline
Relevant experience is dened as:
• any professional position involving data acquisition,
management or handling (e.g. database manager) or
• any professional position involving analytics (e.g. data
analyst)
Evidence requirements will be a CV and an employer
provided statement of service in relation to relevant
experience.
Cognate Discipline is dened as a degree in one of the
following disciplines:
a science allied with medicine, including
• medicine
• nursing
• dentistry
• physiotherapy
• optometry
• biomedical/biological
science
• pharmacy
• public health
• veterinary science
• biology
• biochemistry
• statistics
• mathematical sciences
• computer science
• psychology
• (health) economics
• data science
• other (case-by-case basis)
1
2
3
5
4
Check you meet the entry requirements described on page 16 and English
language requirements (unsw.edu.au/english-requirements-policy)
Apply online at UNSW Apply Online (applyonline.unsw.edu.au)
• Register with UNSW Apply Online
• Select the Master of Science 9372, Graduate Diploma 5372 or Graduate Certificate 7372 program
• Select the study delivery mode on-campus (partially online) or distance (fully online)
• Upload necessary documents (certified or original documents, CV and employer statement
if applicable)
• Pay application processing fee
• Submit application
Track application at UNSW Apply Online
Enrol in courses online
• Follow the prompts in the “getting started” website to enrol in courses online
Accept your offer online
• Successful applicants will be emailed a Letter of Offer
• Follow the instructions in the Letter of Offer and accept your offer at
www.gettingstarted.unsw.edu.au
Admissions and Enrolment Process
Apply now for entry into Term 1 or Term 3, 2021
Applications for Term 1, 2021 close
on 29 January 2021 for International
students and 5 February 2021 for
Domestic students.
Applications for Term 3, 2021 close
on 27 August 2021 for International
students and 3 September 2021 for
Domestic students.
17
Health Data Science
18
UNSW Sydney
School per UOC per 6 UOC
School of Medical Sciences (HDAT) A$660 A$3960
School of Medical Sciences (PHAR) A$590 A$3540
School of Computer Science and Engineering (COMP/BINF) A$745 A$4470
Graduate School of Biomedical Engineering (BIOM) A$745 A$4470
School of Mathematics and Statistics (MATH) A$650 A$3900
School
per UOC per 6 UOC
School of Medical Sciences (HDAT) A$815 A$4890
School of Medical Sciences (PHAR) A$655 A$3930
School of Computer Science and Engineering (COMP/BINF) A$985 A$5910
Graduate School of Biomedical Engineering (BIOM) A$985 A$5910
School of Mathematics and Statistics (MATH) A$930 A$5580
Program Total Cost of Program
Graduate Certicate A$15,840
Graduate Diploma A$31,680
Master of Science A$46,260 - $49,050
Indicative Fees
Domestic
International
The total cost of the Master of Science program will depend on your selection of courses in the nal stage of the
program. The indicative 2021 tuition fees are subject to change on an annual basis.
Financial assistance for Domestic students
FEE-HELP is an Australian Government loan scheme that assists eligible full fee-paying students to pay all or
part of their tuition fees. For more information, please visit: student.unsw.edu.au/fee-help.
Program Total Cost of Program
Graduate Certicate A$19,560
Graduate Diploma A$39,120
Master of Science A$55,800 - $61,740
For more information regarding UNSW tuition and other student fees, please visit: student.unsw.edu.au/fees
19
Health Data Science
Program Total Cost of Program
Graduate Certicate A$15,840
Graduate Diploma A$31,680
Master of Science A$46,260 - $49,050
2020
6 March Applications open
30 November Domestic Admissions deadline for Term 1
Commonwealth Supported Place consideration
Key Dates
The full UNSW academic calendar is available at student.unsw.edu.au/new-calendar-dates
2021
29 January International applications for Term 1, 2021 close
5 February Domestic applications for Term 1, 2021 close
12 February Induction and Welcome
15 February - 13 May Term 1 Teaching Period
14 May - 30 May Term Break
31 May - 26 August Term 2 Teaching Period
30 July Domestic Admissions deadline for Term 3
Commonwealth Supported Place consideration
27 August International applications for Term 3, 2021 close
27 August - 12 September Term Break
3 September Domestic applications for Term 3, 2021 close
13 September - 09 December Term 3 Teaching Period
