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Australia / New Zealand students

International students

COURSE

Master of Predictive Analytics

MC-PREDAN

TAUGHT BY: School of Electrical Engineering, Computing and Mathematical Sciences (EECMS)

DURATION

Duration of the course with a full-time study load. Studying part-time will extend the duration of your course.

If duration is not available (N/A) the offering may be part of a larger course.

2years

CREDIT

A full-time study load usually consists of 200 credits (approximately eight units) per year, with 100 credits (approximately four units) in each semester.

400

About offering

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.

Our MPA course is 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, mining and 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 multidisciplinary degree, in which students can choose from three 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 (Business and Law)

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 (Business and Law)

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.

What you'll learn

  • use research to apply an understanding of the theoretical background basis of data analytics and to allow the data processing of unstructured data, including all aspects of cluster analysis to produce a qualified interpretation of the data
  • analyse an unstructured data set or problem in a logical, rational and critical way; identify alternative methods of solving the issue and select the optimum solution that provides the best outcomes for both industry and the community
  • obtain, evaluate and apply relevant processing algorithms to unstructured data from a range of sources to solve or predict an operational problem prior to or during an occurrence
  • 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 the implementation of data analysis and prediction developments and the continual operational improvement of data generating systems throughout their lifecycle
  • appreciate the need for, and develop, a lifelong learning skills strategy in relation to enhanced personal and company performance
  • recognise the global nature of the predictive analytics 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

Why study Master of Predictive Analytics

  • 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

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. 

Career information

This course will help you become a:

  • data analyst
  • operation and business consultant in resource engineering/asset management/finance.

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 with an excellent understanding of the science and application of predictive analytics
  • 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.

Admission criteria

Australia / New Zealand students

International students

A recognised bachelor degree.

Bachelor's degree in science, engineering, business or commerce from a recognised university.

English language proficiency

Curtin requires all applicants to demonstrate proficiency in English. Specific English requirements for this course are as outlined in the IELTS table below. Additional information on how you can meet the English requirement can be found on the English proficiency page.

IELTS Academic (International English Language Testing System)
Writing 6.0
Speaking 6.0
Reading 6.0
Listening 6.0
Overall band score 6.5

Course prerequisites

Bachelor's degree in science, engineering, business or commerce from a recognised university.

Certificate in Advanced English (CAE): 176

Pearson Test of English Academic: 60

Advanced standing / credit transfer / recognition of prior learning

Australia / New Zealand students

International students

Credit for recognised learning (CRL) is the term Curtin uses to describe advanced standing, academic credit or recognition of prior learning.

You may be entitled to credit for recognised learning for formal, non-formal or informal learning.

Formal learning is learning that takes place through a structured program of learning that leads to the full or partial achievement of an officially recognised qualification. Recognised institutions include, but are not limited to, RTO providers and universities. Non-formal learning is adult and community education. Informal learning can include on the job learning or various kinds of work and life experience.

Credit can reduce the amount of study needed to complete a degree.

For further information, please visit our credit for recognised learning webpage or contact our CRL Office on crl@curtin.edu.au or 1300 222 888.

Credit for recognised learning (CRL) is the term Curtin uses to describe advanced standing, academic credit or recognition of prior learning.

You may be entitled to credit for recognised learning for formal, non-formal or informal learning.

Formal learning is learning that takes place through a structured program of learning that leads to the full or partial achievement of an officially recognised qualification. Recognised institutions include, but are not limited to, RTO providers and universities. Non-formal learning is adult and community education. Informal learning can include on the job learning or various kinds of work and life experience.

Credit can reduce the amount of study needed to complete a degree.

For further information, please see our credit for previous study website or contact tne_pathways@curtin.edu.au.

Fees and charges

Australia / New Zealand students

International students

No domestic fee data currently available.

International student indicative fees for 2018

International onshore – fee paying
Offer letter (100 credit) published fee $15,500*
Indicative year 1 fee $31,400*
Total indicative course fee $67,800*
Indicative essential incidental course fee N/A

The indicative fees shown above apply to international students studying on-campus in Western Australia. For information about fees at other locations please visit Curtin International’s offshore site.

How to apply

Please review information on how to apply for the campus of your choice:

Please note that each campus has different application deadlines. Please view our application deadlines page for further information.

Apply now

Where to get further information

Applicants applying with an International Baccalaureate (IB) Diploma

Find information on how you can apply to study at Curtin with an International Baccalaureate (IB) Diploma.

Applicants with other international qualifications

Find information on what qualifications you can use to apply for a Curtin course.

  • The offering information on this website applies only to future students. Current students should refer to faculty handbooks for current or past course information. View courses information disclaimer.

  • Curtin course code: MC-PREDAN
  • CRICOS code: 092977C
  • This offering was last updated on: August 14, 2018