Master of Data Science

What you will study

The course comprises 96 credit points structured into three parts: Part A. Foundations for advanced data science studies, Part B. Core master's study and Part C. Advanced practice

• Part A. Foundations for advanced data science studies
• Part B. Core master's study
• Part C. Advanced practice

These studies focus on professional or scholarly work that can contribute to a portfolio of professional development. You have two options: 

• A research pathway including a thesis. If you wish to use this master's course as a pathway to a higher degree through research, you should take this first option. 
• A coursework program involving advanced study and an industry experience studio project.

Master's entry points

Depending on prior qualifications, you may receive entry-level credit, which determines your point of entry to the course:

• If admitted at entry level 1, you complete 96 credit points, comprising Part A, Part B and Part C.
• If admitted at entry level 2, you complete 72 credit points, comprising Part B and Part C.

Note: If you are eligible for credit for prior studies, you may elect not to receive the credit and complete one of the higher credit-point options.

Entry requirements

Duration: two years full-time, four years part-time (96 points to complete)    

• An Australian bachelor degree (or equivalent), not necessarily in IT, with at least a credit (60%) average.

Duration: 1.5 years full-time, 3 years part-time (72 points to complete)  

• An Australian bachelor degree (or equivalent) in a cognate discipline relating to IT, or a business, engineering or science degree with an IT major including python programming, databases, algorithms, computer architecture, operating systems and networks, and mathematics (including calculus, linear algebra and probability and statistics) with at least a credit (60%) average.

English entry requirements

• IELTS (Academic): 6.5 Overall score with minimum band scores: Listening 6.0, Reading 6.0, Writing 6.0 and Speaking 6.0.
• Pearson Test of English (Academic): 58 Overall score, with minimum scores: Listening 50, Reading 50, Speaking 50 and Writing 50.
• TOEFL Internet-based test: 79 Overall score, with minimum scores: Reading 13, Listening 12, Speaking 18 and Writing 21.
• Equivalent approved English test.

Outcomes

Learning outcomes

• Analyse the lifecycle of data through an organisation.
• Apply the major theories in the field of data analysis and data exploration to some characteristic problems.
• Investigate, analyse, document and communicate the core issues and requirements in developing data analysis capability in a global organisation.
• Demonstrate an understanding of data science to a level of depth and sophistication consistent with senior professional practice.
• Review, synthesise, apply and evaluate contemporary data science theories through independent research and a research thesis, or by utilising research methods for scholarly or professional purposes.
• Document and communicate ethical and legal issues and norms in privacy and security, and other areas of community impact with regards to the practice of data science.