Master by coursework
Learn how to correlate probability assessments, handle the big data issues of the future and make informed decisions in your business or industry.
- Master of Predictive Analytics
This course is 2 years' full-time or equivalent part-time study.
- 2 years full-time
A full-time study load usually consists of 200 credits (approximately eight units) per year, with 100 credits (approximately four units) in each semester.
The Commonwealth Register of Institutions and Courses for Overseas Students (CRICOS) Code indicates a registered program offered to international students studying in Australia on student visas.
The Curtin campuses or teaching locations where units of this course are offered.
- Curtin Perth
Select your preferred campus:
If COVID-19 restrictions apply, we may need to make changes to units and/or delivery modes.
Broad career options
Choose from four majors to learn about specific applications of predictive analytics.
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The Master of Predictive Analytics addresses the growing demand for data scientists who have the right blend of technical and analytical skills to meet the challenge of big data analytics. It is currently the only master degree course in predictive analytics in Australia.
It is a multidisciplinary degree, in which you can choose from four majors to learn about specific application domains. It introduces advanced skills in data management, mining and visualisation, decision methods and predictive analytics, with a focus on their applications to different disciplines, such as engineering, networking, business and finance.
You will have opportunities to work on industry-sponsored projects, and participate in Curtin partnerships through Innovation Central Perth and the Curtin Institute for Computation.
Upon completion of this course, you will be well placed to handle the ‘big data’ issues of the future, understand how to overlay historical and prediction data with production, financial and other data and correlate probability assessments to make better informed decisions.
Resource Operations Analytics
The Resource Operations Analytics major is for petroleum and mining engineers. It gives you the ability to analyse, interpret and utilise complex data analytics relating to resource assets and operations. This major will improve your operational business decision-making, resulting in maximised asset productivity and enhanced business growth.
This major was the first course in Australia to apply data analytics and big data concepts in practice to optimise operational engineering.
Finance and Investment Analytics
The Finance and Investment Analytics major embeds economic and financial econometric analysis within the data and predictive analytic framework. It aims to help you become a data and predictive analytics expert with a working knowledge in economic, finance and business data.
You will learn to apply your skillset to different business situations, and help inform finance and investment forecasts.
Internet of Things
The explosion of embedded and connected smart devices, systems and technologies in our lives has created an opportunity to connect every ‘thing’ to the Internet. The resultant data collection and connectivity generates huge amounts of data, which needs to be analysed and potentially responded to in real-time. This is disrupting and transforming every industry around the world.
The Internet of Things Major draws on the fundamentals of Predictive Analytics to teach you the underlying principles and architecture of the Internet of Things, its networks, devices, programming, data and security.
The Data Science Major consolidates data science and predictive analytics skills through core machine learning and project units along with a range of optional units. These units will extend your knowledge in many areas including artificial intelligence, statistics, programming, security and automation.
This major is applicable to employment in data analytics across a wide range of fields.
- Data analytics is used to analyse data in order to draw conclusions – whereas predictive analytics is a newly emerging field that allows us to utilise this data in order to predict future outcomes, allowing companies to make better informed decisions and execute efficient strategies on disruptive technologies.
- Predictive analytics can be applied to many fields of interest, from resource operations engineering, asset management and productivity, and finance and investment, to actuarial science and health economics.
How this course will make you industry ready
- You can tailor your degree to suit your career field.
- You’ll gain advanced knowledge and undertake professional practice.
- You’ll work on cutting-edge projects in the space of innovation and commercialisation, drawing on sophisticated research methods and techniques.
- You’ll develop an excellent understanding of the science and application of predictive analytics, and how to improve and develop prediction software.
- Computer scientist
- Data analyst
- Business consultant
- Operations consultant.
- Big Data
- Resources engineering.
What you'll learn
- obtain, evaluate and apply relevant processing algorithms to data from a range of sources to solve or predict an operational problem prior to or during an occurrence; use research to apply an understanding of the theoretical basis of data analytics to produce a qualified interpretation of the data.
- find innovative approaches to improving operations through the combination, generation and analysis of dataanalyse problems in a logical, rational and critical way; identify alternative methods of solving issues and select optimal solutions that provide the best outcomes for both industry and the community.
- communicate effectively with a wide range of people from different discipline areas, professional positions and countries; communicate data analysis findings in a variety of ways via written, verbal or electronic communications; evaluate and utilise appropriate technology for data analysis and prediction development; appreciate the need for, and develop, a lifelong learning skills strategy in relation to enhanced personal and company performance.
- recognise the global nature of predictive analytics in industry and apply global standard practices and skills for acceptable prediction outcomes regardless of discipline or geographical location.
- practise appropriate industry data collection methodologies; work and apply discipline knowledge within the given social or industrial framework; with consideration of and respect for cultural diversity, indigenous perspectives and individual human rights.
- apply lessons learnt in a professional manner in all areas of prediction design, demonstrating leadership and ethical behaviour at all times.
A recognised bachelor degree.
Curtin requires all applicants to demonstrate proficiency in English. Specific English requirements for this course are outlined in the IELTS table below.
|IELTS Academic (International English Language Testing System)|
|Overall band score||6.0|
At Curtin, we understand that everyone’s study journey has been different.
You may have already studied some of the units (subjects) listed in your Curtin course, or you may have work experience that matches the degree requirements.
If this applies to you, you can apply for credit for recognised learning (CRL), which means your previous study is recognised and matched against a similar unit in your intended Curtin course.
A successful CRL application exempts you from having to complete certain units within your course and means you could finish your degree in a shorter amount of time.
CRL is also known as recognition of prior learning, advanced standing and credit transfer.
Use the CRL search to find out how much CRL you qualify for, or contact us at:
Webform: Submit here
Curtin Connect: 1300 222 888
Fees & charges
Domestic fee paying postgraduate
Fee year: 2023
What is a domestic fee-paying (DFP) place?
A domestic fee-paying place is a place at university which is not Commonwealth supported, that is, not subsidised by the Australian Government.
Domestic fee paying students will be charged tuition fees and may be eligible for FEE-HELP assistance for all or part of those tuition fees.
Fees are indicative only.
* Based on a first-year full-time study load of 200 credits. The total cost will depend on your course options (i.e. units selected and time taken to complete).
For start dates, please view the academic calendar.
- On campus
- On campus
- On campus
- On campus
- On campus
All endeavours are made to ensure location information for courses is up to date but please note they are subject to change.
The University reserves the right to withdraw any unit of study or program which it offers, to impose limitations on enrolment in any unit or program, and/or to vary arrangements for any program.
How to apply
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Please note that each campus has different application deadlines. Please view our application deadlines page for further information.Apply now
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