MASTERS BY COURSEWORK
Master of Data Science
Melbourne City
Harness the power of big data with the Master of Data Science. With a mix of analytics and computer science you’ll be central to business decision-making, corporate strategy and government planning.
We live in a data-driven world that’s generating huge volumes of information at ever increasing rates, via social media, financial transactions, telecommunications, and even scientific discovery.
Handling this big data is consequently a top priority for all of us, whether you’re in business, industry, government, or even individually, as citizens and consumers.
The emerging interdisciplinary field of data science combines areas of computer science with mathematical statistics and domain expertise to manage and analyse this data.
Though incorporating statistical methods, data science puts a greater emphasis on the specialised computational skills required to manage and analyse big data from sources such as social media, sensors, mobile and transaction data.
Data scientists develop the capability to derive insight and opportunity from the vast repositories of data that many organisations collect.
They help organisations in all sectors of the economy to make sense of these very large volumes of data; enabling businesses to gain a competitive edge, governments to deliver more targeted services, and research teams make new discoveries.
Data science and the analysis of big data could appeal to people from a whole range of backgrounds, such as graduates from computing, mathematics, science, engineering or health.
Despite sharing a number of courses with the Master of Analytics, the Master of Data Science has a significant core of both computer science and statistics.
This prepares graduates for a career working in a field that is driving scientific research, economic growth, public policy and corporate strategy, using cloud technologies to aid the management and analysis of very big data sets.
There are opportunities to experience an industry internship, and you will also undertake a capstone project that gives you hands-on, practical experience analysing data in a project setting.
The emphasis is on working within a corporate environment and integrating all the knowledge you’ve acquired throughout the program into a solid skills base to take into your professional life.
After being exposed to the latest theoretical and practical expertise, graduates of the Master of Data Science are expected to become influential leaders within their organisations.
It’s been called the “sexiest job of the 21st century”, but whether or not that’s true, data scientists that can manage and make sense of big data are among the most important roles in the information age.
- 1.5 years full-time or 3 years part-time (with advanced standing)
- 2 years full-time or 4 years part-time (without advanced standing)
- 1 year full-time or 2 years part-time, Graduate Diploma of Data Science available as exit award.
International student visa holders can only study full-time.
- 1.5 years full-time or 3 years part-time (with advanced standing)
- 2 years full-time or 4 years part-time (without advanced standing)
- 1 year full-time or 2 years part-time, Graduate Diploma of Data Science available as exit award.
RMIT is committed to providing students with an education that strongly links formal learning with workplace experience.
You will learn through a broad mix of study modes including lectures, tutorials, practical classes, project work and seminars using face-to-face, online and other flexible delivery mechanisms.
The program has substantial links with the data science profession and related areas of industry, both within Australia and internationally.
The courses are taught by academics with strong research backgrounds and links to industry, as well as sessional teachers and guest lecturers currently working in industry.
Employers and industry professionals with data science expertise are members of our Industry Advisory Committee and have contributed to the initial development and ongoing improvement of the program.
Their involvement ensures that the program remains relevant to your needs as a graduate and the needs of graduate employers.
Academics teaching in the program are also involved with RMIT Data Analytics Lab, which is a hub for advanced data analytics projects, supporting researchers and helping Australian and Victorian businesses compete on a global scale.
Originally launched as a joint initiative between RMIT University and National ICT Australia (NICTA) - now Data61 - the lab applies text, user and data analytics research to industry-driven projects that solve problems and provide efficiencies in key areas including health, logistics, smart cities, environment and security.
With its industry links and data focus, the lab is an excellent resource for master students to gain networking access to researchers in the field and project partners.
In addition, local ‘meet-ups’ provide clear pathways into the data science community on both a social and professional level.
Those involved in teaching the program, whether from industry or academia, attend these meet-ups and expect that their students will join those that follow their interests too.
Any organisation handling large volumes of data needs qualified data scientists.
Organisations in all sectors of the economy - IT sector, business sector, science and engineering, government, medical - can gain a competitive edge by better managing and analysing their data.
As data science is still a new and emerging field, the roles available for data scientists are consequently quite varied and diverse.
As well as the title of data scientist, other positions include analytics specialist, business intelligence analyst/developer, data analyst, data architect, data engineer, data miner, research scientist and web analyst.
RMIT is situated in Melbourne’s central business district, with the adjacent biomedical and knowledge precinct to the north - both areas being particular hubs for data science employment.
While industry expertise is at the heart of program delivery, studying at RMIT also means you are physically located close to Melbourne’s data professionals, with many opportunities for developing links to local industry and jobs, including meet-ups, events and seminars, as well as other networking occasions.
After being exposed to the latest theoretical and practical expertise, graduates of the Master of Data Science are expected to become influential leaders within their organisations.
Professional recognition and accreditation
Students and graduates can join the Institute of Analytics Professionals of Australia (IAPA). IAPA is the professional organisation for the analytics industry in Australia, incorporating business analytics and data mining across multiple disciplines and sectors.
Associate Professor James Thom is the program manager and has expertise in big data and database systems.
Dr Vural Aksakalli is a data analytics specialist in statistical machine learning.
Dr James Baglin is an expert in data visualisation, biostatistics and educational technology.
Dr Zhifeng Bao is expert in making heterogeneous cross-domain data usable in an efficient way.
Associate Professor Lawrence Cavedon has expertise in speech interfaces, biomedical/health analytics and artificial intelligence.
Dr Jeffrey Chan is an expert in machine learning, data mining, social network analysis and user modelling.
Associate Professor Vic Ciesielski has expertise in artificial intelligence, evolutionary computing, machine vision and data mining.
Dr Haydar Demirhan is a statistician with expertise in Bayesian statistics and time series analysis.
Dr Stelios Georgiou has expertise in statistical experiment designs and regression analysis.
Associate Professor Tao Gu focuses on mobile/pervasive computing, big data analytics and online social networks.
Associate Professor Melih Ozlen has expertise in mathematical modelling and decision analysis.
Dr Yongli Ren has expertise in web search, collaborative filtering, recommender systems and data mining.
Associate Professor Falk Scholer is an expert in information retrieval systems.
Dr Yan Wang specialises in statistical analyses of epidemiological and ecological spatial data.
Associate Professor Xiuzhen (Jenny) Zhang is an expert in databases, data mining and data analytics.
330 Swanston Street (cnr La Trobe Street)
Melbourne VIC 3000
Tel. +61 3 9925 2260
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Program and course information
Key content
Gain the skills to manage and analyse big data
As a major growth area, you'll be in demand for a wide variety roles managing very large collections of data to derive insights that ultimately benefit society.