Master of Predictive Analytics

Program Overview

Master of Predictive Analytics

Overview

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.

Course Essentials

• Qualification: Master of Predictive Analytics
• Duration: 2 years full-time or equivalent part-time study
• 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. Total credit: 400
• CRICOS: 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. CRICOS code: C
• Location: The Curtin campuses or teaching locations where units of this course are offered. Location: Curtin Perth

Admission Criteria

• Recognised Bachelor Degree: A recognised bachelor degree is required for admission.
• English Requirements: Curtin requires all applicants to demonstrate proficiency in English. Specific English requirements for this course are outlined in the IELTS table below.
  • Writing: 6
  • Speaking: 6
  • Reading: 6
  • Listening: 6
  • Overall band score: 6

Fees and Scholarships

• Domestic Fee Paying Postgraduate: $35,032* (2025 Fee year)
  • *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).

Location and How to Apply

• Location: Curtin Perth
• How to Apply: Please review information on how to apply for the campus of your choice. Each campus has different application deadlines.

Course Structure

• Overview: The Master of Predictive Analytics introduces advanced skills in data management, mining, and visualisation, decision methods, and predictive analytics, with a focus on their applications to different disciplines.
• Majors:
  • Resource Operations Analytics: For petroleum and mining engineers, focusing on analysing, interpreting, and utilising complex data analytics relating to resource assets and operations.
  • Finance and Investment Analytics: Embeds economic and financial econometric analysis within the data and predictive analytic framework, aiming to help you become a data and predictive analytics expert with a working knowledge in economic, finance, and business data.
  • Internet of Things: Draws on the fundamentals of Predictive Analytics to teach the underlying principles and architecture of the Internet of Things, its networks, devices, programming, data, and security.
  • Data Science: Consolidates data science and predictive analytics skills through core machine learning and project units along with a range of optional units.

Career Outcomes

• Careers: Computer scientist, data analyst, business consultant, operations consultant.
• Industries: Big Data, Finance, 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.
• Find innovative approaches to improving operations through the combination, generation, and analysis of data.
• Communicate effectively with a wide range of people from different discipline areas, professional positions, and countries.
• 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.