Master of Predictive Analytics
|Study method||Full-time or part-time|
|Duration||2 years full time|
The Master of Predictive Analytics (MPA) addresses the growing demand of data analysts/scientists that have the right blend of technical and analytical skills to meet the challenge of big data analytics.
Currently the only Master's course in Australia in Predictive Analytics, the curriculum emphasises the integration of technical and business skills. It introduces advanced skills in data management, data mining, data visualisation, decision methods and predictive analytics with a focus on their applications to different disciplines, such as engineering, management, business and finance.
It is a truly multi-disciplinary degree, in which students can choose from 3 streams to learn about specific application domains. They will also have opportunities to work on projects from various industries and organisations, or on analytical problems through industry sponsored projects, Innovation Central Perth, the Curtin Institution for Computation, or others.
Resource Operations Engineering (Science & Engineering)
The Resource Operations Engineering stream aims to develop petroleum and mining engineers who will have the ability to analyse, interpret and utilise complex data analytics relating to resource assets and operations, in order to improve their operational business decision-making resulting in maximised asset productivity and business growth.
This stream will provide the first distinct course in Australia to apply data analytics and big data concepts in practice to optimise operational engineering decision using disruptive technologies for enhanced productivity.
Finance and Investment Analytics (Curtin Business School)
The Finance and Investment Analytics stream embeds economic and financial econometric analysis within the data and predictive analytic framework. It produces data and predictive analytics experts with working knowledge in economic, finance and business data, thus allowing them to apply the skillset in the business context.
Asset Management & Productivity (Curtin Business School)
The Asset Management & Productivity stream aims to develop future managers able to analyse, interpret and utilise data relating to the assets and operations of an organisation. It provides students with skills necessary to enhance business effectiveness and provide leadership in productivity improvement and asset-utilisation.
The stream will be focus on the role that disruptive technologies will play and the implications for strategic/operational management and leadership.
Admission criteria for Australian and New Zealand students
- Recognised bachelor degree.
English language proficiency
|IELTS Academic (International English Language Testing System)|
|Overall band score||6.5|
Other qualifications may also satisfy our English language admission criteria. Please view our English language requirements page to find out more.
Fees for Australian and New Zealand students
|2018||Domestic fee paying Domestic fee-paying - 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.||$26,200*|
Fees are indicative first year only.
* Based on a first-year full-time study load of 200 credits. 400 credits are needed to graduate from this course. The total cost will depend on your course options (i.e. units selected and time taken to complete).
Applications for recognition of prior learning (RPL) are assessed on an individual basis. Try our RPL search to find out what credit you might be eligible for.
Learn about Credit for Recognised Learning (CRL)
The course will develop Resource Operations Engineers with a strong knowledge of data analytics, Scientists with the ability to improve and develop new prediction software, Business graduates who will have an excellent understanding of the science and application of predictive analytics, and Finance graduates with an ability to apply predictive analytics to finance and investment forecasting decision making processes.
In addition, these graduates will be well placed to handle the ‘big data’ issues of the future, understand how to overlay historical and prediction data with supply chain financial and other business data and correlate probability assessments for better informed decisions.
This course will help you become a:
- Data Analyst
- Operation and business consultant in resource engineering/asset management/finance
- The Master of Predictive Analytics (Coursework) prepares students to apply advanced knowledge for professional practice, scholarship and further learning corresponding to
- AQF level 9 qualifications.
- 2-year structure of the Master Degree contains a range of discipline streams for students to choose from.
- Projects incorporating the use of research methods and techniques will be undertaken to demonstrate advanced knowledge and professional skills at the postgraduate level.
More information about Predictive Analytics