Masters Qualifying Program (MQP), Leading to Master of Predictive Analytics - Resource Operations Analytics Navitas Group - Curtin College, Australia

The Master Qualifying Program (MQP) is a one-semester non-award program designed for students who already have a tertiary qualification but who do not qualify for direct entry into a Master’s degree at Curtin University. The emphasis is on developing the skills necessary to succeed in postgraduate-level studies.

Leading to: Master of Predictive Analytics
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

Major : 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.

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.