Warning! This is a restricted degree. Subject enrolment is only available to students admitted into this degree.

Degree structure

Degree details

At the completion of this degree students will be able to:

  1. understand the economic foundations of financial markets and how these foundations can manifest in reality via market data
  2. analyse market data in a logical, rational and critical way and correctly identify the information in the data
  3. obtain, evaluate and apply relevant econometric techniques to market data in order to obtain useful predictions to aid business decisions
  4. communicate effectively with a wide range of people from different disciplines, professional positions and countries; communicate financial data analytic findings via both verbal and written media.
  5. evaluate and utilize appropriate technologies to construct data analytics for financial market data and to compute useful predictions that aid business decisions
  6. appreciate the need for, and develop, a lifelong learning skills strategy to pursue continuing development
  7. recognize the global nature of financial and investment analytics and apply international standard practices and skills to produce excellent prediction outcomes
  8. practice appropriate data collection methodologies in finance industry with consideration of and respect for cultural diversity, indigenous perspectives and individual human rights
  9. apply lessons learnt in all areas of finance and investment analytics, demonstrating leadership and ethical behavior at all times.

Business consultancy Accenture has forecast that 4000 workers (all involved with data analytics) are required by the end of 2016 in Australia for servicing the Liquefied Natural Gas (LNG) trains, rising to 9000 for the 21 LNG trains alone by 2018. ConocoPhillips is presently developing its Darwin LNG project using predictive analytics (LNG18, Perth). The Shell ‘Bridge’ project in the Gulf of Mexico automated 70% of a number of offshore platforms saving $50M over three years by reducing operations/maintenance/downtime, while the Curtin-Cisco Internet of Everything Innovation Centre (CIIC) was established in 2015 to develop research into the ability to apply predictive data analytics over the internet for optimised decision making by industry.

Higher education

Applicants require a bachelor degree (or equivalent) in a quantitative discipline, such as mathematics/statistics, computer science, engineering, economics or finance, or at least 5 years relevant work experience in a role requiring quantitative knowledge/skill. Applicants must also meet Curtin’s English language proficiency requirements.

English Proficiency Requirements

Applicants for a Graduate Certificate are required to meet University academic and English language entry standards; details are provided at http://futurestudents.curtin.edu.au.

*** Please Note: If any academic or legal document is not in English, you must provide a colour scan of the non-English documents, including a colour scan of the official English translations of these documents. This is for comparison purposes. ***

Credit for Recognised Learning (CRL) is assessed on individual merit and is awarded for different types of learning, for example, studies you have previously completed or for relevant work experience.

Students must be admitted in an award degree of study before lodging their completed CRL application, along with all required supporting documentation for a formal assessment.

To apply for CRL, please visit the Curtin University website: https://study.curtin.edu.au/credit/

It is important to note accepted documentation includes scans of the original Transcripts and/or Award Certificate; front and back; in colour; and original size. For further information see the scanned documents and certification requirements and guidelines.

More information about the policies and procedures related to CRL assessment and appealing a CRL assessment outcome can be found in the Credit for Recognised Learning manual (PDF).

If you have any questions, please contact opencurtin@curtin.edu.au

The Graduate Certificate in Finance and Investment Analytics 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.

This degree is designed to prepare you for entry to the multi-disciplinary Predictive Data Analytics profession, in which many operations are automated and controlled remotely. Predictive Analytics is the study of data in order to predict and subsequently optimise management decisions. It has been developed in close collaboration with business and the resources industry to ensure the syllabus is comprehensive and will meet legal registration requirements. It provides a broad-based study of general data analytics including computing and business organisation for the financial and investment industry.

Award Requirements

To qualify for the Graduate Certificate in Finance and Investment Analytics, students must complete 4 core subjects.