phd data science australia

• Melbourne Centre for Data Science
• Education and Training
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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 Fellows

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

Research: Supervisors:   School/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: School/Faculty:

Research:  Supervisors: School/Faculty

Research : Galactic Astronomy Supervisor : Prof Michele Trenti School/Faculty: 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/Faculty: 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 : Optimising the Laser Direct Infra-Red (LDIR) Chemical Imaging System for microplastics Analysis of environmental and biological samples Supervisors : Dr Brad Clarke and Professor Amanda Ellis School/Faculty: Faculty of Science, Chemistry, Australian Laboratory for Emerging Contaminants

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/Faculty: 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/Faculty: School of Mathematics and Statistics - Biosciences, Faculty of Science

Research :  A Hierarchical Markov-switching Model for Bull and Bear Markets Supervisors: Dr Yong Song and A/Prof Ole Maneesoonthorn School/Faculty: 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/Faculty: School of Agriculture, Food and Ecosystem Sciences, Faculty of Science

Research:  Explaining the Uncertainty in AI-Assisted Decision Making Supervisors: Prof Tim Miller, Prof Liz Sonenberg and Dr Ronal Singh School/Faculty: 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/Faculty: 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 School/Faculty: Florey Department of Neuroscience and Mental Health, Faculty of Medicine, Dental and Health Sciences

School/Faculty:  Quantifying and Mitigating Digital Overdiagnosis Supervisors: Dr Daniel Capurro and Dr Douglas Pires School/Faculty: 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 School/Faculty: 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 School/Faculty: Faculty of Architecture, Building and Planning

Research :  Continual learning by Efficient Neural Network Synthesis Supervisors : Prof Saman Halgamuge and Prof Denny Oetomo School/Faculty: 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 School/Faculty: 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/Faculty: 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/Faculty: 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/Faculty: 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/Faculty: 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/Faculty: 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/Faculty: 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/Faculty: 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 School/Faculty: 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 School/Faculty: Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences

Research :  Time domain gravitational lensing Supervisor : Prof Rachel Webster School/Faculty: 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/Faculty: 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 School/Faculty: 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 School/Faculty: Melbourne School of Population and Global Health, Faculty of Medicine, Dental and Health Sciences

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Data science

Shape our world through data

If you’re thinking about studying data science, you’re probably already aware of its power to shape our world. As billions of devices feed data to central databases, businesses and governments require experts to interpret that data. Data scientists have become one of the most in-demand professionals globally because they have the unique skills to unlock hidden insights within data.

Our degrees in data science will prepare you to compile, interpret and act on data in ways that make a big impact. These degrees combine training in mathematical methods, statistics, computing and business decision-making with communication skills. You’ll gain the theoretical and practical skills required to enter the field of data analysis where networking, decision-making, strategic thinking and attention to detail are essential.

Harness the expertise of three schools

Our suite of data science and decisions programs are taught across three world-leading faculties at UNSW – Science, Engineering and the UNSW Business School.

UNSW is ranked in the top 50 worldwide for Mathematics, Engineering & Technology, and Economics and Econometrics. 

Career opportunities in data science

Data science is a multidisciplinary field of study that blends mathematical methods, statistics, computing, business decisions and communication. Data scientists translate user data into insights that help companies understand their audience and personalise their products or services. Uncovering new insights and predicting trends has become vital in the retail, environmental, not-for-profit, IT and professional services sector.

Career opportunities in data science span across many industries from start-ups to established corporations including Coles, AirBnB, Spotify, Netflix and Greenpeace, to name a few. As a data scientist, you might be asked to calculate the price of new products for the market, determine warehouse or retail employee schedules, make predictions of environmental phenomena and model the spread of new diseases.

Our programs

You can study data science in the following undergraduate degree:

• Bachelor of Data Science and Decisions

The honours program in quantitative data science is intended for students who have completed the quantitative data science stream in program 3959 - the Bachelor of Data Science and Decisions. It's also open to other students from other programs with a suitable mix of undergraduate courses. 

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

• Graduate Certificate in Data Science  (Online)
• Graduate Certificate in Data Science and Decisions
• Graduate Diploma in Data Science  (Online)
• Graduate Diploma in Data Science and Decisions
• Master of Data Science  (Online)
• Master of Data Science and Decisions

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

• Doctor of Philosophy (PhD)
• Masters by Research

Visit the School of Mathematics & Statistics

*QS World University Rankings by Subject 2023

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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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• 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] .

Personalise your experience

Graduate Coursework

Graduate Diploma in Data Science

• Arrow-right #1 in Australia for Computer Science and Statistics
• 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 at UniSA

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Interested in Data Science?

Enter the exciting area of Big Data and Data Science with a growing demand for data scientists across numerous industry sectors.

The volume of data generated from new advances in technologies and systems such as mobile phones, social media sites, online searches, satellites and the Internet of Things has brought about the new and exciting area of Big Data.

Data Scientists are in strong demand to manage, analyse and use the data collected to create predictive models. However, there is currently a significant shortage of data scientists globally and companies are actively looking for talented professionals to analyse big data and inform their business decisions. In Australia, there's currently high demand for data scientists 1 . 

The University of South Australia offers flexible postgraduate programs in data science, including a Graduate Certificate, Graduate Diploma and Master degree.

1 Deloitte Access Economics, The future of work: Occupational and education trends in data science in Australia, February 2018.

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Key features.

• Data Science degrees

Your career

• No.1 in SA for graduate careers in I.T 1
• Flexible delivery options, including online ^ and face-to-face learning.
• Designed in conjunction with industry, including the Institute of Analytics Professionals of Australia and SAS .
• Provides graduates with the analytical and technical skills to effectively extract useful information from Big Data to support the management and operations of an organisation.
• Learn software tools used in the industry and work on industry sourced analytics projects.
• Professional development and built in opportunities to work on analytics projects sourced from industry.
• SAS Academic Specialisation Certification on completion with a digital badge provided.
• These programs are offered in the form of a nested suite (Graduate Certificate, Graduate Diploma and Master) with each qualification extending to the next, allowing you to easily transition up to and back from a master level qualification.

^ Domestic students only 

1 ComparED (QILT) Student Experience Survey 2021-22, Computing and Information Systems – Skills Development Indicator (Undergraduate). SA public universities.

The UniSA Data Science programs will provide you with the knowledge and computational skills to become a highly sought-after analytics professional and data scientist.

The aim of these programs is to produce graduates with the analytic and technical skills to effectively use Python, Hadoop, R and SAS to analyse big data and extract useful information to support the management and operations of an organisation.

You will benefit from coursework designed in consultation with industry, including with the Institute of Analytics Professionals of Australia and leader in analytics software and solutions - SAS.

In these programs, you will undertake courses across a broad range of Data Science topics, which will provide you with hands-on experience in data analytic techniques for real-world challenges. You will develop skills in managing big data and data infrastructure, data cleaning, machine learning, statistics, predictive analytics, data visualisation, data privacy and security, teamwork, presentation of results, professional writing, and project management.

UniSA offers the following Data Science degrees

Choose your level of study

The demand for analytic skills is supported by research from the U.S Bureau of Labor Statistics, which projects that employment for information security analysts will grow between 32% between 2018-2028 - higher than average for all other occupations.

While the demand for analytic skills crosses multiple industries, fields such as marketing, healthcare, government and finance are expected to experience a particularly strong need for analytics professionals.  

Data science qualifications can lead to a variety of careers in the following areas:

• data scientist
• business data analyst
• machine learning analyst
• machine learning engineer
• big data visualiser
• information security analyst
• data engineer
• statistician

Richard Shanahan, Master of Data Science graduate

Australian students

Phone: +61 8 8302 2376 Enquiry: unisa.edu.au/enquiry

International students

Phone: +61 8 9627 4854 Enquiry: unisa.edu.au/enquiry

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.

Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.

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PhD by Distance

Become world-ready, from wherever you are in the world, with a UTS PhD by distance mode.

If your research is based internationally but you want access to UTS's world-leading researchers and reputation, you've been required to complete a PhD for your career but you don't want to relocate, or you want to expand your global networks to create real-world research impact, the PhD by distance mode might be right for you. 

A UTS PhD by distance mode empowers you to develop your future as a researcher in a global context —all without applying for a visa or subletting your apartment.

What you can expect

• full enrolment as a PhD student at UTS
• supervision and mentorship from a panel of experts
• administrative support and guidance from your faculty
• PhD from a Top 100 global university
• access to UTS online facilities and resources
• high-quality research training and professional skills development 

Study requirements

The study expectations of PhD by distance mode students are the same as on-campus students. This means that you will:

• need to meet the same admissions requirements , including English-language proficiency
• be supported by a panel of supervisors
• complete any mandatory coursework
• go through the same candidature stage assessments as other PhDs from your faculty
• be subject to the same student rules that govern PhD study at UTS
• have the same expected timelines for your study as other PhD students
• be liable for the same course fees as other international students unless you are assessed as eligible for a tuition fee scholarship

Support and development

As a fully-enrolled UTS student, you’ll have access to a variety of forms of support for your research, your professional development as a researcher, and your own health and wellbeing, including:

• UTS Library’s extensive digital catalogue and one-on-one researcher support
• online skills development workshops and training modules, including in areas like research writing, software training, and project management
• personalised advice from UTS Careers and access to online resources 
• inclusion in the online research student Teams channel

Find more information about the support available to you as a distance mode PhD student  (PDF, 0.8MB).  Please note that not all benefits available to on-campus students exist in digital form or translate to distance study.

Find out more: Distance mode is available for international students. Read all admissions requirements → There are scholarships that can help with fees. Find out more about the costs of a PhD by distance → Ready for the next steps? Explore the application process → 

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.

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• a citizen of Australia or New Zealand,
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• a holder of an Australian permanent humanitarian visa.

You're an international student if you are:

• intending to study on a student visa,
• not a citizen of Australia or New Zealand,
• not an Australian permanent resident, or
• a temporary resident (visa status) of Australia.

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Master of Data Science

Become job-ready and prepared to tackle complex data science challenges and play a leading role in the future development of data science solutions globally.

This is the most comprehensive data science program in Australia, combining high-level analytical and technical skills with industry-specific knowledge and essential professional attributes. It's also one of the few programs in the world to offer you a cross-disciplinary education with direct industry contact and practical experience in the exciting field of data science.

You'll study advanced topics from computing, statistics and mathematics, and your choice of electives from business, finance, health or science. Across the program, you'll learn how to make informed decisions in data-intensive environments.

You'll use relevant big data tools and technologies and develop essential knowledge about the ethical use of data, legal considerations, communication and more. In your final year, you'll also complete projects relevant to industry.

Since data underpins the digital economy, there are enormous opportunities for data scientists to revolutionise the way we work, live and communicate. With a Master of Data Science you'll be expertly prepared to work in Australia or overseas across a large number of industry sectors, government agencies and high technology companies, as well as consulting and market research firms.

If you come from a business background, our  Master of Business Analytics  may be the program for you. While both programs address the function of data analytics, the Master of Data Science provides technical expertise, while the Master of Business Analytics will teach you how and why to make business decisions using data.

State-of-the-art facilities

The school is home to facilities including a big data lab with relevant tools and technologies; more than 400 Windows, Unix and Apple workstations; more than 20 specialist computer labs (including 10 with 24-hour access); and a university-wide 802.11ac Wi-Fi network.

Program highlights

• Benefit from the most comprehensive data science program in Australia.
• Study advanced topics from computing, statistics and mathematics.
• Learn how to make informed decisions in data-intensive environments.
• Graduate with a combination of high-level technical skills and a capacity for creative and disruptive thinking.

1 in Queensland for engineering and technology

QS World University Rankings 2024

78 in the world for engineering and technology

How you'll learn

Your learning experiences are designed to best suit the learning outcomes of the courses you choose.

• Research experience

What you'll study

At UQ, degrees are called 'programs' and subjects are called 'courses'. Here's a sample of the courses you could study in this program:

• Introduction to Data Science
• Responsible Data Science
• Data Analytics at Scale
• Machine Learning

See courses and program structure

Career possibilities

Postgraduate study can take you anywhere. Here are some of the careers you could be on your way to:

• Market researcher
• Data scientist
• Risk analyst
• Supply chain analyst
• Business intelligence analyst
• Big data engineer
• Portfolio manager
• Actuarial and analytics consultant
• Software developer

Average annual salary range

Data Scientist

seek.com.au

Data Engineer

Next steps after graduation.

Master of Data Science graduates will be expertly prepared to work in Australia or overseas across a large number of industry sectors, government agencies, and technology companies, as well as in consulting and market-research firms.

In China alone, the market for big data is experiencing an annual growth of more than 30 per cent based on expenditure of 10 Billion RMB since 2015.

The median salary of data scientists is typically reported in six-digit figures and in China it is reported to be 3.5 times higher than in comparable positions.

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6-minute read

Entry requirements

It's possible to complete this degree in 2 years or 1.5 years depending on your qualifications and experience.

You can apply for any duration as long as you meet the entry requirements. You may also be eligible to apply for credit or exemptions to shorten your degree further. You'll graduate with the same qualification no matter how long you take to complete the degree.

2-year degree (32 units of study)

To be eligible to complete the degree in 2 years full-time (or part-time equivalent) full-time (only available as full-time study) , you'll need:

• a bachelor's degree (or equivalent) in a relevant discipline (see below), or
• a bachelor's degree (or equivalent) which includes all of the relevant discipline content (see below), or
• to have successfully completed 3 years of study towards an approved qualification at an overseas partner institution, with a formal pathway to the Master of Data Science.

You must have a grade point average (GPA) of 5.0 on a 7-point scale in your previous qualification

1.5-year degree (24 units of study)

If you have relevant prior learning or experience, you can reduce the number of courses you need to complete and graduate in less time.

To be eligible to complete the degree in 1.5 years full-time (or part-time equivalent) full-time (only available as full-time study) , you'll need:

• a bachelor's degree (or equivalent) in a relevant discipline (see below) with studies in at least 3 courses in mathematics, 2 courses in statistics or probability and 3 courses in computer sciences.

Relevant disciplines for previous qualifications

Relevant disciplines include information technology, computer science, statistics, mathematics, engineering, physics and actuarial studies.

If your bachelor's degree was not awarded in a relevant discipline, you must have successfully completed the following discipline content in your tertiary studies (for entry into the 2 year degree only):

• calculus or advanced mathematics or higher mathematics or mathematical analysis, and
• at least 2 courses from the following content areas (courses must be from two different content areas):
• linear algebra
• programming or C Language or object oriented (OOP) or java/software engineering
• databases or data structures or algorithms or data mining
• probability or statistics
• computational mathematics or numerical analysis or differential equations or multivariate analysis

GPA equivalent

Select where you studied and your qualification to see the GPA equivalent you need to be considered for this program.

Use the GPA equivalent as a guide. When you apply, we’ll calculate your GPA using the UQ grading scale. Any failing grades will be included. Entry requirements are subject to change.

Equivalent subjects

English language requirements.

IELTS overall 6.5; reading 6; writing 6; speaking 6; listening 6. For other English Language Proficiency Tests and Scores approved for UQ

TOEFL iBT (including Paper Edition) - Overall 87, listening 19, reading 19, writing 21 and speaking 19.

PTE Academic - Overall Score of 64 and 60 in all sub bands.

BE - A minimum overall grade of 4 plus a minimum grade of C in all macro skills.

CES - Overall 176 and 169 in all sub bands.

OET is not accepted.

There are other ways to meet the English language requirements. For some programs, additional conditions apply.

Learn how to meet the English language requirements

Student visas

International students who are accepted into full-time study in the Master of Data Science are eligible to apply for an Australian student visa (subclass 500).

There are a number of requirements you must satisfy before a visa is granted, including the Genuine Student (GS) requirement.

Learn more about student visas

Need help meeting the entry requirements?

We can help you meet the academic or English language requirements for your preferred program.

If you haven’t completed the prerequisites, we can help.

Learn about pathway options

Fees and Scholarships

Indicative annual fee.

Approximate yearly cost of tuition (16 units). Your fees will vary according to your selected courses and study load. Fees are reviewed each year and may increase.

Learn more about postgraduate fees

Approximate yearly cost of full-time tuition (16 units). Your fees will vary according to your study load. Fees are reviewed each year and may increase.

AUD $51,200

Government assistance, financial aid.

As an international student, you might be eligible for financial aid – either from your home country, or from the Australian Government.

Learn more about financial aid

Domestic places in the Master of Data Science are Commonwealth Supported. This means the cost of your education is shared between you and the Australian Government.

Instead of tuition fees, Commonwealth Supported students pay what are called student contribution amounts.

HECS-HELP is an Australian Government loan scheme to assist eligible students with the cost of their student contribution amounts.

Learn more about HECS-HELP

Centrelink support

The Australian Government offers a number of income-support payments to eligible Australian university students.

Learn about Centrelink payments for students

Scholarships

You may be eligible for more than 100 scholarships, including:

Applying online

All international applications should be submitted to UQ. If you prefer, you can use an  approved UQ agent in your country .

The program code for the Master of Data Science is  5660 .

This program is available in multiple durations. You can apply for any duration as long as you meet the entry requirements.

When you apply, select your preferred duration. If you don't meet the requirements for your first preference, we'll automatically consider you for entry into a longer duration.

Find out more about applying for postgraduate coursework study

All domestic applications should be submitted to UQ.

The program code for the Master of Data Science is 5660 .

When you apply, select your preferred duration. You can also ask us to consider you for a longer duration if you don't meet the entry requirements for your first preference.

Important dates

The closing date for this program is:

• To commence study in semester 2 - May 31 of the year of commencement.
• To commence study in semester 1 - November 30 of the previous year.

To learn more about UQ dates, including semester start dates, view the Academic Calendar .

• To commence study in Semester 1 - January 31 of the year of commencement.
• To commence study in Semester 2 - June 30 of the year of commencement.

Aboriginal and Torres Strait Islander applicants

For support with applying – or if you have any questions about university life – get in touch with our Aboriginal and Torres Strait Islander Studies Unit.

Contact the ATSIS Unit

Explore other programs

Master of business analytics, master of computer science (management), master of financial mathematics, master of commerce, express yourself. and your interest..

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