phd data science in australia

• Melbourne Centre for Data Science
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• Doctoral Academy

MCDS Doctoral Academy

A campus wide multi-disciplinary program for PhD students using data science in their work.

About the program

The MCDS Doctoral Academy aims to bring together a campus wide multi-disciplinary cohort of PhD students (MCDS Doctoral Academy Fellows) to share their research, domain challenges and thoughts around the use, implementation and application of data science in their fields.

The Academy will support the Fellows through peer-learning opportunities, masterclasses, workshops, discussions, opportunities for presentations and more. Such a mixed environment will expose students to different disciplinary perspectives and stimulate the development of new approaches to the ways in which data science is incorporated in research.

The program is ideal for students who:

• Enjoy engaging in cross-disciplinary research skills development and desire to build practical skills
• Seek to explore ways that data science methodologies can be used and advanced within their research
• Appreciate building a community of young researchers who consider data science matters in their field at the University of Melbourne

2023 Doctoral Academy

2023 Academy Applications are now closed.

Join a group of like-minded, interdisciplinary PhD students for sessions and workshops discussing data science in different domains, best practice for interdisciplinary research, ethics, research translation, communications, project scope, careers and more,

Application & information

2023 Doctoral Academy Fellows

Research: Detectability of Climate Change Signals in Observations and Models Supervisors:  Dr Andrew King, Dr Josephine Brown, and A/Prof Ed Hawkins School of Geography, Earth and Atmospheric Sciences Faculty of Science

Research: Supervisors:   Faculty

Research: Human-Building Interaction Model for Energy Use of University Student Accommodation in Melbourne Supervisors:  A/Prof Christhina Candido and  A/Prof Hemanta Doloi School/Faculty: School of Architecture, Building and Planning - Melbourne School of Design

Research: Interaction Matters: Automated Evaluation and Socing of Interactive Ability in Second Language Dialogue Supervisors:  Prof Carsten Roever, Dr Jey Han Lau Schools/Faculty: School of Languages and Linguistics & School of Computing and Information Systems -Natural Language Processing Group

Research:  Supervisors:

Research:   Supervisors:   Faculty

Research: Galactic Astronomy Supervisor: Prof Michele Trenti School of Physics Faculty of Science

Research: Earth observation and AI for urban building footprint extraction Supervisors: A/Prof Jagannath Aryal and Prof Abbas Rajabifard School of Electrical, Mechanical and Infrastructure Engineering Faculty of Engineering and Information Technology

Research: Modelling the impact of mass casualty incidents on the provision and utilisation of emergency department services Supervisors: A/Prof Nic Geard, Dr Daniel Capurro

School/Faculty: School of Computing and Information Systems, Faculty of Engineering and IT

Research: Modelling the transmission and evolution of emerging and re-emerging sexually transmissible pathogens Supervisors: Deborah Williamson, Ben Howden and George Taiaroa

School/Faculty: Department of Infectious Diseases - Faculty of Medicine, Dentistry and Health Sciences

Research: Prenatal, birth and early childhood risk and protective factors for infections in children Supervisors: Prof David Burgner, Dr Jessica Miller, Prof Lars Henning Pedersen, Dr Maria Magnus

School/Faculty: Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences

Research: The role of motivational factors, coping, and psychological wellbeing on preservice teachers’ commitment to the teaching profession

Supervisors: A/Prof. Gavin Slemp, Prof. Dianne Vella-Brodrick

School/Faculty: Centre for Wellbeing Science, Faculty of Education

2022 Doctoral Academy Fellows

Research: Severe convective winds in Australia and their sensitivity to climate change Supervisor: Prof Todd Lane School of Geography, Earth and Atmospheric Sciences  Faculty of Science

Research: Developing and benchmarking methods for multi-omics single-cell quantitative trait locus mapping in complex diseases Supervisors: Dr Davis McCarthy and Dr Heejung Shim School of Mathematics and Statistics - Biosciences  Faculty of Science

Research:  A Hierarchical Markov-switching Model for Bull and Bear Markets Supervisor: Dr Yong Song and A/Prof Ole Maneesoonthorn Department of Economics  Faculty of Business and Economics

Research:  Improving practical applications of dynamic occupancy models Supervisors: Dr Gurutzeta Guillera-Arroita and Dr Natalie Briscoe School of Agriculture, Food and Ecosystem Sciences Faculty of Science of Science

Research:  Explaining the Uncertainty in AI-Assisted Decision Making Supervisors: Prof Tim Miller, Prof Liz Sonenberg and Dr Ronal Singh School of Computing and Information Systems  Faculty of Engineering and Information Technology

Research:  Optimising and Improving Species Detections Supervisors: Prof Mick McCarthy and Dr Gurutzeta Guillera-Arroita School of Agriculture, Food and Ecosystem Sciences Faculty of Science

Research:  Using modern Machine Learning for the probabilistic modelling of dementia Supervisors: Dr Victor Fedyashov, Dr Benjamin Goudey, Dr. Daniel Beck and Dr. Pierrick Bourgeat Florey Department of Neuroscience and Mental Health Faculty of Medicine, Dental and Health Sciences

Research:  Quantifying and Mitigating Digital Overdiagnosis Supervisors: Dr Daniel Capurro and Dr Douglas Pires School of Computing and Information Systems Faculty of Engineering and Information Technology

Research:  Toward better characterisation of restricted and repetitive behaviours in Autistic Youth Supervisors: Dr Mirko Uljarevic and A/Prof Luke Smillie Complex Human Data Hub School of Psychological Sciences  Faculty of Medicine, Dentistry and Health Sciences

Research:  A data-driven model of urban form evolution: quantitative analysis of long-term spatial transformation in the city centres of Melbourne, Barcelona and New York Supervisors: Prof Justyna Karakiewicz and Dr Fjalar de Haan Faculty of Architecture, Building and Planning

Research:  Continual learning by Efficient Neural Network Synthesis Supervisors: Prof Saman Halgamuge and Prof Denny Oetomo Department of Mechanical Engineering Faculty of Engineering and Information Technology

Research:  Risk prediction for metachronous colorectal cancer among colorectal cancer survivors Supervisors: Prof Mark Jenkins, A/Prof Aung Ko Win, Dr Amalia Karahalios, and Prof Alex Boussioutas Melbourne School of Population and Global Health Faculty of Medicine, Dental and Health Sciences

2021 Doctoral Academy Fellows

Research: Spatial causal inference analysis Supervisors: Dr Martin Tomko and Prof Stephan Winter School of Infrastructure Engineering  Faculty of Engineering and Information Technology

Research: Non gaussianity and secondary cosmic microwave background anisotropies in the south pole telescope data Supervisor: Dr Christian L. Reichardt School of Physics  Faculty of Science

Research:  Analysis of an ongoing conflict through the lens of media frames Supervisors: Prof Robert Hassan and Dr Philip Pond School of Culture and Communication  Faculty of Arts

Research:  Reproducibility and transparency of model-based research in applied ecology and conservation decision-making Supervisors: Dr Libby Rumpff, Prof Fiona Fidler, and Dr Hannah Fraser School of BioSciences  Faculty of Science

Research:  Natural disturbance-based models for silviculture in the Victorian Central Highlands Supervisors: Prof Patrick Baker and A/Prof Craig Nitschke School of Agriculture, Food and Ecosystem Sciences Faculty of Science

Research:  Instance space analysis of quantum optimisation algorithms Supervisors: Prof Kate Smith-Miles and Dr Charles Hill School of Mathematics and Statistics  Faculty of Science

Research:  A data driven model of agenda setting in Australian politics Supervisors: Prof Andrew Turpin and Dr Gosia Mikolajczak School of Computing and Information Systems Faculty of Engineering and Information Technology

Research:  An evidence-based approach to climate change experimentation in international city networks Supervisors: Dr Kathryn Davidson and Prof Michele Acuto Melbourne School of Design Faculty of Architecture, Building and Planning

Research:  An examination of the motivations underlying, and consequences of, social network dynamics for the resolution of environmental collective action problems, using "gamified" experiments and simulations (agent-based models) Supervisors: Prof Yoshihisa Kashima and A/Prof Andrew Perfors Melbourne School of Psychological Sciences  Faculty of Medicine, Dentistry and Health Sciences

Research:  Time domain gravitational lensing Supervisor: Prof Rachel Webster School of Physics  Faculty of Science

Research:  Model-based and Hybrid Multiagent Reinforcement Learning in Internet of Things Supervisors: Prof Tansu Alpcan and Dr Sarah M. Erfani School of Electrical and Electronic Engineering  Faculty of Engineering and Information Technology

Research:  Exploring embodied values in Singaporean paintings through technical examination and data analysis Supervisors: Dr Nicole Tse and Prof Robyn Sloggett Grimwade Centre for Cultural Materials Conservation Faculty of Arts

Research:  Understanding breast cancer risk via an automated measure based on mammographic textural features: Cirrus Supervisors: Prof John Hopper, Dr Shuai Li, Dr Gillian Dite, and Dr Tuong Linh Nguyen Melbourne School of Population and Global Health Faculty of Medicine, Dental and Health Sciences

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7 Australian Universities That Offers PhD in Data Science

Obtaining a PhD in Data Science helps deepen your knowledge in the field that is undoubtedly producing the greatest number of lucrative jobs in the world presently. Some of the best universities in the world are located in Australia, making it an ideal place to get your PhD in data science.

Now, let’s look at seven best universities in Australia that offer a PhD degree in Data Science.

Australian Universities Offering PhD in Data Science

University of melbourne.

University of Melbourne (UniMelb), was founded in 1853 and has its primary campus at the Melbourne suburb of Parkville, with its other campuses situated across the state of Victoria. The 2019 Times Higher Education World University Rankings rated University of Melbourne as the best university in the country and 32 nd  in the world.

READ ALSO:  Top 8 Institutions in Delhi to Study Data Science

The school has a student population of over 50,000, with 36 percent from outside Australia. Multiple Nobel laureates and Australian PMs have graduated from UniMelb.

Students can obtain a PhD in Data Science from this university. The program, which takes place at the Parkville campus, is a 4-year duration for a full-time student and eight years for a part-time student. It doesn’t involve any necessary coursework, only requested by the students.

University of Queensland

The University of Queensland (UQ) was established in 1909 with its main campus, a 281 acres of land at St Lucia, Queensland lying on the bank of Brisbane River. The student population at University of Queensland is over 53,000, with about 18,000 being international students.

Queensland University also offers a PhD degree in Data Science with School of Information Technology and Electrical Engineering (ITEE) of the university. The program is only available in full-time format and takes a duration of 3-4 years. All through the program, the work of student is managed by two or more supervisors.

3. Monash University

Monash University, a public research institution, always rank among the top 100 universities in the world. Monash University was established in the year 1958, and presently has a student population north of 78,000.

READ ALSO:  Top 10 Courses to Study in Australia for Getting Jobs

The university has two large campuses in Melbourne, at the suburbs of Clayton and Caulfield, while others lie across the state of Victoria.

Students can study to obtain a PhD in Data Science in Australia by coming up with a research problem that lies within the field that gets approved by the Faculty of Information Technology. A supervisory team that consists of at least two supervisors will support a student all through the program. Students will carry out a set of necessary coursework that covers advanced training in IT research methods. The course takes a duration of four years in full-time format and eight years for part-time students.

4. University of Sydney

The University of Sydney was founded in 1850, making it the oldest university in Australia. QS World Rankings ranks the University of Sydney first in the Land Down Under and fifth globally for graduate employability. The school’s main campus occupies around 178 acres of land in the inner-west Sydney suburbs of Camperdown and Darlington.

University of Sydney has a student population of over 61,000. This prestigious university is the alma mater of two Nobel laureates and seven Aussie prime ministers.

Students can obtain a PhD degree in Data Science in the University’s Faculty of Engineering by presenting a thesis that is an original contribution to the field of Data Science. Data Science students will have access to two research centers:

• Centre for Distributed and High-Performance Computing and
• UBTECH Sydney Artificial Intelligence Centre.

The PhD program in Australia is available in only full-time study and takes a duration of three years.

5. RMIT University

RMIT University started as a night school in 1887 and attained the status of a University in 1992. Presently, this public research university has a student population of about 87,000. According to QS World Rankings, RMIT University is the second in Australia and 15 th  in the whole world for graduate employability.

RMIT University also offers PhD in Data Science and Analytics. PhD students with a subject in the particular research area use the RMIT Data Analytics Lab as their research incubator. The program takes about three to four years to complete full-time format and six to eight years for part-time format.

University of Western Australia

The University of Western Australia (UWA), a public research university, was established in 1911 as the sixth university in the land of kangaroos. Its main campus is situated at the suburb of Crawley in Perth, the capital of Western Australia. The university has two other campuses sited at the Perth suburb of Claremont and at the port city of Albany. The school has a student population north of 24,000.

UWA also provides students with the opportunity to earn a Ph.D. degree in Data Science through an independent, managed research program. The program is commenced after a research topic has been mutually agreed upon by a student, their supervisor, head of the Faculty of Engineering and Mathematical Sciences, and the Board of the Graduate Research School. It is available in both full-time that covers a duration of four years.

University of Technology Sydney

The University of Technology Sydney (UTS), was established in 1988. The university has been recognized as one of the top ten young universities in the world by QS World Rankings. Its main campus is located at the Sydney suburb of Ultimo with a student population of about 46,000, with 15,000 of this total being international students.

Students can pursue a PhD degree in Analytics and Data Science at UTS, under the Faculty of Transdisciplinary Innovation. The degree is carry out fully by thesis, a work in the scope of 80,000 to 100,000 words. The program, which takes four years in full-time study mode and eight in part-time, is arranged in three phases:

• In the first phase, students and their supervisor discuss a study plan that includes details on kinds of support that will be required.
• In the second phase, the students develops their individual program of research.
• In the third phase, the students prepares and submits the thesis.

Bottom line

These are the top seven (7) universities in Australia that offers PhD in Data Science. Most of the schools offers part-time programs, that would allow certain students who are engaged in other tasks not to be left out. Though the tuition fees at these universities weren’t stated, but they are quite affordable for international students who wish to obtain a PhD in Data Science and Analytics.

Article source: uscolleginternational.com,        Image source: meetup.com

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The Master of Data Science and Innovation is a ground-breaking program of study and the first transdisciplinary data science degree offered in Australia where creativity and innovation are integral components.

Graduate Certificate in Data Science and Innovation

Take a fast-track pathway into the data science industry. Graduate in six months.

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Dr Simon Knight, Senior Lecturer: The Master of Data Science and Innovation and the Grad Cert as well, you're actually building up a professional portfolio as you go.

So the idea is that you're doing things that actually match what professionals would be doing and because of the way that we designed it, you'll be working in a range of different subjects to develop different kinds of skills around working with data and communicating that data.

Priyanka Srinivasa, Master of Data Science & Innovation graduate: I started looking out for universities and UTS Master of Data Science and Innovation really attracted me because of the way it’s taught. It’s more industry-based experience. Everything you learn you put into practise from day one.

Joseph Tristram, Master of Data Science & Innovation current student: We get the opportunity to work with a lot of established and well known corporates, governments and a lot of the projects cross over a lot of different industries as well, so it was a great opportunity for me to learn a lot of transferable skills.

Dr Simon Knight: The Master of Data Science and Innovation is a transdisciplinary course. We see data science as a team sport. We actually need to be applying skills from different contexts, different disciplines, different professions in order to understand the problem spaces that we’re applying data science in.

Joseph Tristram: So the benefits of having a good cohort is that we have that diversity of thought. This is important because everyone brings their own experiences, their own challenges and perspectives.

I've made some amazing friendships throughout the course and, you know, the relationships I’ve built with the lecturers themselves, I think that's some of the memories I’m always going to retain.

Dr Shibani Antonette, lecturer: So as we know data science is a rapidly growing field which means there are methods, techniques coming out every single day and we want students to be prepared to learn the most advanced technique there is and keep them updated so which is why the course is constantly reviewed by industry professionals, experts and leading research that happens in the field.

Dr Simon Knight: We’re not just about the technical side of data science, we’re also about the human centred approach. Understanding where our data has come from, how we’re modelling it and how it’s then going to be used in a practical context where it might be informing decisions that have impact on people.

Priyanka Srinivasa: Being a part of something that is going to help create a better future is what interested me. When you’re trying to build something, the thing that makes you irreplaceable is actually the ethics you bring into the data, your perspective and your creativity and innovation. That's my favourite part of the course.

Learn more at uts.edu.au/mdsi

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PhD scholarship in Statistics and Data Analytics

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PhD scholarship in Mathematical Sciences

A student with a strong background in statistics with Honours or Masters, excellent computing skills in R or Python, and an interest in working with biochemists and medical researchers.

Value and duration

$28854 per annum for 3 years

Opening date

Applications are now open.

Closing date

Number of scholarships.

One (1) scholarship available

Eligibility

Domestic students

How to apply

Initially contact the university and the project supervisor for suitability.

Project supervisor: Inge Koch  [email protected]

Further information

This project includes big functional data from proteomics MALDI mass spectrometry imaging with an external supervisor and data expert from South Australia. Close collaboration with the South Australian lab will be part of the supervision.

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• PhD Scholarship

Applications are now Closed for QUT Centre for Data Science 2024 PhD Scholarship

• Complex data analysis
• Data for discovery
• Human centred AI
• Models and algorithms
• Responsible data science
• Business and engineering systems
• Environment and Natural Systems
• Government Systems
• Health and Biological Systems
• Social Systems
• Sports Systems

You will receive a tax free scholarship of $33,637 per annum for three years.

This stipend will be paid fortnightly and indexed annually. This scholarship is for full-time study and can be used to support living costs. A six-month extension to the scholarship may be possible, subject to approval.

A successful international student will be considered for a research degree tuition fee sponsorship, if successful in receiving the scholarship.

Eligibility

To be eligible for this scholarship, you must meet the entry requirements for a Doctor of Philosophy  (PhD) at QUT, and not be in receipt of another living allowance scholarship to undertake the research degree.

In addition, this scholarship has specific eligibility conditions including:

• applicants must nominate a proposed supervisor, and must make contact with the supervisor prior to submitting an Expression of Interest
• have excellent written and verbal communication skills
• demonstrate the knowledge and skills for the PhD project
• applicants must be able to commence, on shore, by 31 July 2024.

How to apply

Fill out this application form where you will be asked to:

• Provide an up-to-date curriculum vitae (CV) and a copy of your latest academic transcript, or relevant research skills if applicable.
• Indicate a research area in CDS that you will be interested to work in
• Provide names of your proposed supervisor(s)
• Advise your student and PhD application status.

What happens next?

If successful in your scholarship application, you will be invited to submit an expression of interest (EOI) to study a PhD at QUT if you have not yet done so. See more information about this process here: How to apply for a research degree

If your EOI is successful, you will be invited to submit a full application including a research proposal to finalise your application.

Application Closing Date

Scholarship applications close 5pm Sunday 26 November 2023 .

The scholarship will be governed by QUTPRA rules (QUT Postgraduate Research Award (Domestic) or QUT Postgraduate Research Award (International) but will only include provisions for up to an additional 12 weeks of paid sick leave, paid maternity leave and paid extensions.

There are additional milestones that will need to be achieved as a part of this scholarship that will be discussed with you at time of application.

If you have further questions on the scholarship please contact [email protected] , if you have questions about completing a PhD at QUT please contact [email protected] .

PhD in Statistics

The PhD program in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS) equips graduates with knowledge of developments in theoretical and applied statistics. The PhD program draws upon the diverse expertise of academic staff across the University. The School’s PhD candidates will undertake independent research on a specialised research topic.

The program is focused on developing candidates for a career in academia, government or industry. Positions in government or industry may include researchers in scientific, medical or health research organisations; researchers or analysts within government agencies, such as the Australian Bureau of Statistics and Australian Institute of Health and Welfare, or departments of Health, Agriculture, Education, Finance and Treasury; researchers or quantitative data analysts within the corporate sector, including banking, finance and insurance, pharmaceutical, and energy and mining sectors; and management and statistical consultants.

CRICOS #: 048345A

Duration: 2 to 4 years full time (4 to 8 years part time)

Before you submit an application for entry to the program, you should:

• ensure you meet the admission requirements outlined below
• identify potential supervisors – that is, one or two statistics academics at ANU who conduct research in your area of interest.

You can find information on researchers and their research areas in the  ANU researchers database  and on the  RSFAS Statistics faculty  page.

While other ANU schools may recommend contacting potential supervisors before submitting an application,  this is not required  for entry into RSFAS’s PhD programs. Instead, you only need to list the name(s) of potential supervisors in your online application form.

Potential supervisors cannot guarantee entry into the PhD program. Admission will depend on the strength of your application relative to others in the pool.

After you’ve completed the steps above, you can proceed with an  online application .

Application deadlines

The first semester of the ANU academic year starts in February, and the second semester starts in July. While all applications for first semester entry must be submitted  before 31 October,  international applicants wishing to be considered for an  ANU scholarship  should submit their applications  before   31 August .

To be considered for a scholarship, your application must be accompanied by all the supporting documents listed below, including the referee reports. Request for referee reports are triggered and sent to your nominated referees at the time of submission of program application. It is thus important that you submit your application in advance (2-3 weeks) to allow time for your referees to provide their reports prior to the scholarship deadline.

If you’re currently completing an academic degree and haven’t yet received your final results and transcript, you should still submit all available documents before the deadline, and forward remaining results once you receive them. We won’t make a final decision on your application until we’ve received all the required documents.

The admission requirements for the PhD program in Statistics reflect the advanced knowledge that candidates will need to undertake the coursework component of the degree, and the research experience and skills needed to successfully undertake and complete the research thesis.

The minimum qualification requirement for admission to the PhD program in Statistics is:

• an Australian Bachelor degree (or equivalent) with First Class Honours or Second Class Honours Division A in statistics (or a related discipline), or
• another qualification (e.g. a Master degree) with a substantial research thesis component that the RSFAS HDR (higher degree by research) committee is satisfied is equivalent or superior to a degree mentioned in (a), or
• a combination of qualifications and professional experience that the RSFAS HDR committee is satisfied is equivalent or superior to a degree mentioned in (a).

Admission to the PhD program in Statistics is competitive and we can only admit a limited number of applicants each year. Meeting the minimum entry requirements does not guarantee you a place in the program.

If you don’t have sufficient research experience for entry into the PhD program, you might consider applying to the MPhil program . If you’d like to consider this pathway, contact the RSFAS HDR convenor for more information.

English language requirements

All applicants must satisfy the University’s  English language admission requirements . An international applicant who is not a native English speaker may satisfy these requirements by submitting evidence of an  IELTS  overall score of at least 6.5, and with no component less than 6.0, or a paper-based  TOEFL  score of at least 570, with at least 4.5 in the essay component.

Application and supporting documentation

You must submit your application online via the  ANU Application Manager .

In addition to the standard information required in the online application, you must submit the following supporting documents as part of your application:

• a one-page statement of purpose outlining your motivation to undertake a PhD in Statistics at ANU
• a research proposal – see details below, as well as these guidelines on how to  prepare a persuasive research proposal
• copies of written research work, e.g. honours or Master thesis, research project or published works
• official  TOEFL  or  IELTS  results (where applicable) to demonstrate that you satisfy the University’s  English language requirements .

Research proposal

The online application requires you to submit a research proposal. The proposal should set out an original research idea, provide an introduction or background to your research idea, clearly set out the objectives, data required and expected research methods, and explain why the research is important and the contribution it will make to the discipline. Among other things, the research proposal will require you to demonstrate an understanding of the key literature in your chosen topic area. As a guide, you should aim for between 1,500 and 2,000 words, including a list of key references.

The RSFAS HDR committee uses the research proposal as an indicator to assess the quality and originality of your ideas and your skills in critical thinking. Note that the research proposal does not restrict you to this field of study should you be admitted to the PhD program.

Offers of admission

The RSFAS HDR committee will review all complete applications submitted by the relevant deadline.

If your application is short-listed, you may be required to attend an interview (face to face or online).

We may send you an offer of admission if you satisfy the eligibility criteria and your area of interest matches those of RSFAS academics with supervisory capacity. However, since admission is competitive and supervisory capacity is limited, we won’t send any offers of admission  after the relevant application deadline , irrespective of the date when you submit your application.

The PhD program in Statistics consists of two components –  coursework  and  research .

Candidates undertake the research component after successfully completing the required coursework.

PhD coursework component

PhD candidates may be required to complete up to six semester-length courses during the first year of the program. Required coursework must be completed to a satisfactory level for candidates to progress to the research component. The specific coursework requirements will depend on each candidate’s background and will be determined through discussion with the HDR convenor and the chair of the candidate’s supervisory panel.

Compulsory courses for the PhD in Statistics are:

• STAT8027  Statistical Inference
• STAT8056  Advanced Mathematical Statistics
• STAT7040  Statistical Learning
• STAT7018  Stochastic Modelling

Candidates select up to two electives from graduate-level courses in statistics (or suitable advanced courses from other disciplines) in consultation with the chair of the supervisory panel.

PhD research component

Following the successful completion of coursework, PhD candidates undertake specialised research training and independent research.

Research supervisory panel

When a PhD candidate is admitted to the program, a provisional supervisor is appointed. The provisional supervisor has the responsibility of overseeing the candidate’s progress until a supervisory panel is chosen. During the first year, it is important that candidates start developing their research topic ideas by consulting with their provisional supervisor and other academic staff within RSFAS.

Either in a candidate’s first year of study, or soon after completion of their coursework, a supervisory panel will be chosen. The role of the panel is to assist, advise, and provide support and encouragement to the candidate for a timely and successful completion of the research thesis. The HDR convenor will determine the composition of the supervisory panel in consultation with the candidate.

RSFAS statistics seminar program

The RSFAS statistics seminar program consists of regular seminars presented by national and international researchers. PhD candidates are expected to attend and actively participate in the seminars throughout their candidature.

Research integrity training

Within three to six months of enrolment, all PhD candidates must complete the  Research Integrity Training  and pass the exam. Completion of this course and exam is a compulsory milestone for all PhD candidates.

Thesis proposal review

During the second year, candidates must submit a thesis proposal for review to their supervisory panel and present their proposal as a seminar to the School. The purpose of the thesis proposal review is to assess the originality, significance, adequacy and achievability of the candidate’s thesis plan.

The proposal includes a description of the research to be undertaken in the thesis, and a summary of the thesis structure and time plan.

Successful completion of the thesis proposal review (as determined by the Delegated Authority following consultation with the HDR convenor and supervisory panel) is required to continue in the PhD program.

Annual progress review

It is University policy that each candidate’s progress be reviewed periodically. In each year of their program, PhD candidates are required to submit an  annual plan and report  as a basis for periodic progress review. This document provides details on work completed by the candidate since the previous review, current progress, and any problems that may impact their research. It also outlines the coursework and research the candidate intends to undertake in the following 12 months.

Oral presentation

In their final year, candidates are required to give a final  oral presentation  on their research, usually three months before submitting their thesis.

Read more about  research candidate milestones .

Thesis submission and examination

The culmination of the PhD in Statistics is a written thesis which, upon completion, is submitted for examination. The thesis is examined by examiners who are experts in the relevant field.

For more information on the process, visit our page on  submitting a thesis .

For information about scholarships available to HDR candidates, visit our page on  scholarships and fees .

Read details of some of our alumni’s recent  job placements .

See our list of current  Statistics PhD students .

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Health data science

Discover data-driven solutions to real-world health problems 

We live in a world that is deluged with data. There's a digital revolution occurring across our health systems, which is making health data collection more efficient and accessible. Today, health data are measured in the trillions of gigabytes, and are collected not only at the point-of-care in clinical settings and for research studies, but also through wearable devices and social media. Optimising the use of data will be critical to the future of health and healthcare.

Health data science is the science and art of generating data-driven solutions for critical health issues. It uses the insights uncovered from data to better support clinical care, inform health policy and improve population health. This study area is an emergent discipline, which sits at the intersection of biostatistics, computer science and health. The health data science pipeline includes the comprehension of complex health issues, data wrangling and management, machine learning, data analytics, data modelling and data communication. 

Learn within a world-leading research and teaching institute

Our health data science programs are delivered by the  Centre for Big Data Research in Health (CBDRH)  – the leading Australian and international hub for health research using big data. The CBDRH brings together an interdisciplinary team of staff, who have world-leading expertise in managing, manipulating, analysing and visualising health big data. Using large-scale electronic data than spans the biomedical, clinical and health services domains, the CBDRH is transforming knowledge from data into practical applications within health.

Prepare for career success

A qualification in health data science will teach you to extract crucial knowledge and insights from health big data to inform clinical care and health policy. A qualification in this area will prepare you to make sense of big data to tackle critical health issues facing Australian and global communities.

There is growing demand within the public and private health sector, both in Australia and globally, for professionals with specialised interdisciplinary skills in health data science. The role of a health data scientist is dynamic and always evolving as their work spans across any of the multiple stages of the health data science pipeline. From designing and leading research studies and analysing data, through to building machine learning processes to understand complex health issues, a health data scientist’s work draws on a multiplicity of skills.

Due to the emergent nature of the field, new roles and employment opportunities in this sector are being created all the time. The health data scientist may, for example, manage a team of data analysts to work out processes to gather data, assess how to model the data or devise ways to implement health policy change based on the outcomes of their studies and findings. Our graduates have gone on to careers in hospitals and health services, government agencies, universities and research institutions, pharmaceutical and health technology companies, health insurance companies and health analytics companies.

Our programs

You can study health data science in the following postgraduate coursework programs: 

• Graduate Certificate in Health Data Science
• Graduate Diploma in Health Data Science
• Master of Science in Health Data Science
• Master of Science (Extension) in Health Data Science  

Our health data science programs incorporate leading technologies. In our master's degree program, we offer a course in Clinical Artificial Intelligence , which explores the fundamental concepts of AI systems, the idiosyncrasies of AI for healthcare practice and the ethical/social/legal issues posed by the use of AI technologies in clinical settings. 

You can study health data science in the following postgraduate research degrees:

• Doctor of Philosophy (PhD)
• Masters by Research

We also offer professional development courses in health data science, which are drawn directly from the master's program. The courses can be used as recognition of prior learning towards a postgraduate qualification in health data science. 

Find out more

Visit the Centre for Big Data Research in Health (CBDRH)

UNSW Sydney Health Data Science Information Session

Hear from our panel of expert staff who take a deep dive into our suite of programs, including our brand new Master of Science in Health Data Science (Extension) program.

Personalise your experience

Graduate Coursework

Graduate Diploma in Data Science

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• Course code:   GD-DATASC

Course overview

The Harvard Business Review has labelled data science the " sexiest job of the 21st century ".

We're creating more than 2.5 exabytes of data every day. Someone needs to make sense of it all.

The Graduate Diploma in Data Science is an ideal starting point if you’re interested in joining this booming industry and don't have a background in computer science or statistics.

Your first step towards a new career

Through this course you’ll develop fundamental skills in both computer science and statistics, so you can keep pace with the rapidly changing demands of a data-driven job market – and world.

You’ll be shown how to use statistical tools, techniques and methods along with in-depth analysis and evaluation, learning to solve real-world problems in the data realm.

Option to transition into the Master of Data Science

When you successfully complete the Graduate Diploma, you can supercharge your qualification by enrolling in the Master of Data Science (subject to meeting the requirements).

You’ll receive course credit, meaning that you can complete both the Graduate Diploma and the Master of Data Science in just 2.5 years.

Related study areas

• Computer science
• Data and analytics
• Information technology and computer science
• Mathematics and statistics

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Study Data Science

Take on some of the most sought-after jobs in industry when you study Data Science, with the Bachelor of Science at JCU. Develop knowledge and skills that place you at the forefront of the field, including data visualisation, big data analytics, machine learning and artificial intelligence.

• Bachelor of Science

What is Data Science?

Data science is a union of statistics, computer science and domain application. Its goal is to extract information from data to advance knowledge across industry, business, and societies and support key decision making.

As technology evolves, so, too, does data science. This burgeoning field presents almost limitless opportunities for professional development and lifelong discovery.

Increasingly, industries are collecting and using data to gain insight into their domain. For example, many retail companies use a rewards program to collect information on their customers and use the data to obtain insights into their behaviours and habits, in order to design products and experiences to more completely meet their needs.

Storing, wrangling and analysing large and complex data sets (also known as  data mining ) is a key skill increasingly demanded by employers across the contemporary workplace. Those with this skillset, and the analytical abilities to interpret the results, are becoming more and more valuable. Numerous businesses are beginning to incorporate data into their key decision-making process.

Data science and  statistical modelling may be used to analyse human behaviours and patterns to optimise a range of scenarios including transport routes, urban planning, health services, crime prevention and more.

Big data, algorithms, data visualisation, analytics and more are all terms you hear regularly in the corporate world, but currently, few people have the expertise to really understand data science and apply it to help businesses discover new potential. The sector is ripe for career growth as potential applications continue to develop.

Discover JCU's Bachelor of Science

Delve deeply into data and explore new opportunities as a data scientist with a JCU Bachelor of Science

What does a data scientist do?

Data scientists work first and foremost with computers; however, they are very much dealing with real-world challenges and providing insights that enable the design of highly practical solutions.

As a data scientist, you will have a natural affinity for statistics and mathematics . Don’t worry if it wasn’t your best subject in high school, it’s more about having a genuine interest in the different and widespread applicability of numbers.

Your daily working life as a data scientist may see you calling upon your skills in data analytics.

You may also find yourself working in the field of data visualisation , one whose popularity and applicability to industry is rapidly expanding.  Data scientists who work in data visualisation have the ability to take large swathes of data, understand it, process it, extract value from it and use it for storytelling. They may employ software such as Tableau to analyse and visualise data and create interactive and accessible data models. The stories data tells could be related to consumer patterns, business evolution or employee efficiency. This can be a highly creative part of data science, focused on interpretation and communication, as you analyse data and actively communicate it to stakeholders.

The opportunities within data science are broad, meaning there is a role to suit every personality type. Whether you excel working with statistics or communicating with people, there’s a place within data science for you.

Explore your study options in Data Science

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Gain a solid foundation for university study as you build academic skills, confidence and knowledge. Take classes relevant to your study goals and get a taste of university life in this qualification designed to help you meet the entry requirements of your desired degree. Enter university prepared for success after this one-year course.

• Townsville: February, July
• Cairns: February, July
• Online: February, July

Learn from world-renowned lecturers and engage with experts in your chosen field with an Advanced Science degree from JCU. Develop strong research and communication skills as you investigate the role of science in creating sustainable futures within the Tropics. Model solutions to case studies from an array of biophysical contexts and extend your hands-on learning by completing a research internship or practical placement.

Embrace the exciting world of science. Pair your scientific study with experiences like no other — explore environments like the Great Barrier Reef and the Daintree Rainforest or conduct research in state-of-the-art labs. Learn how to train an AI algorithm to identify flora and fauna species or dig into the earth’s history to find prehistoric remains. Graduate career-ready with advanced practical skills and a drive for discovery.

JCU's online Graduate Certificate of Data Science will give you immediately applicable data science skills. The JCU Graduate Certificate of Data Science is backed by an Industry Advisory Board, made up of data science and tech leaders from across Australia with extensive experience both domestically and abroad.

• Online: January, March, May, July, September, November

JCU's online Graduate Diploma of Data Science can give you the skills and networks to place you at the forefront of the data science revolution. The JCU Graduate Diploma of Data Science is backed by an Industry Advisory Board, made up of data science and tech leaders from across Australia with extensive experience both domestically and abroad.

Gain the skills relevant to succeed in an increasingly connected world through understanding data, analytics, smart devices and smart ecosystems. JCU's online Graduate Diploma of Data Science (IoT) will give you professional skills required in business, government and research.

The JCU Online Master of Data Science is designed to put professionals who recognise the power of data and numbers ahead of the pack. The JCU Master of Data Science is backed by an Industry Advisory Board, made up of data science and tech leaders from across Australia with extensive experience both domestically and abroad.

Unlock a world of potential with the Master of Data Science at JCU and become an authority in the roaring big-data industry. Develop innovative solutions to contemporary problems in data science and mastermind database systems, programs, models, and projects.

• Cairns: March, September
• Brisbane: March, July, November

What jobs are there in data science?

From defence departments to internet start-ups, and financial institutions to sporting teams, almost every industry and profession in the world now uses data and data science to enhance its operations and outcomes.

While Silicon Valley is certainly an option, you may also find data science jobs in businesses and locations far removed from the stereotypical technology hubs.

As a data scientist, you may begin your professional life in a practical and tactical role, executing data queries and creating reports; however, your ability to deliver invaluable business insights may also give you the opportunity to move into highly strategic jobs, either in-house or as a consultant.

With a Bachelor of Science, majoring in Data Science , from JCU, you can embrace careers such as:

• Data scientist
• Data analyst
• Business analyst
• Strategic advisor
• Researcher.

Why study Data Science at JCU?

When you study Data Science at JCU in the Bachelor of Science , or in one of JCU's postgraduate Data Science programs , you will acquire the fundamental knowledge and skills you need in statistics, science and computer science to qualify for a range of data science and analytics roles.

JCU Data Science includes subjects covering the latest areas and developments in the field, including data visualisation, big data and machine learning.

You will also undertake projects, programming and modelling throughout your data science studies that will enable you to provide practical work examples to potential employers that are relevant across a range of industries.

JCU’s small class sizes mean you’ll benefit from personalised learning and one-on-one engagement with your lecturers. Explore opportunities to gain experience before graduation with the JCU Work Integrated Learning program, as well as other internships and industry networking events.

With options to study at our Townsville, Cairns or Singapore campuses, you can consider the domestic and international implications of this rapidly expanding field. Gain experience from across Europe, America or Asia by taking advantage of our Study Abroad programs , to see up-to-the minute developments in your field from an international perspective. The connections and knowledge you gain can carry you into a variety of career opportunities.

Rishitha Asam

Alumni jcu data science.

“This course is helping me to build a career that I was always keen on. I strongly believe that after completion of this course, I will be able to synthesise and evaluate complex information, concepts, methods, and theories from a range of sources. I am certain that this course will be a bridge to fulfill my ambition in becoming a Data Scientist. There’s just so much to learn in this course and I have always been intrigued by it. This is a pretty vast subject and it is often said to be the future of AI. There are plenty of opportunities in this field. I enjoy the challenges in this course.”

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