Master of Data Science (Applied)

For more information about this program please visit the program website.

Enable and lead evidence-based change

Data is today's universal currency. In virtually every organisation, sector and region, it's being gathered, analysed and applied with ever-increasing sophistication to inform better decision-making and guide innovation.

Consequently, top-level data scientists are in high demand worldwide, with opportunities only expected to grow. Our online Master of Data Science (Applied) puts them within reach.

Conducted 100% online over 2.7 years part-time, the degree is highly practical-all tools and techniques explored are fully aligned with industry best-practice. It's also extremely flexible, with course content drawn from, and relevant to, all areas of the economy.

In addition to gaining a strong awareness of data science's capacity for enabling strategic change and quality improvement, you'll develop advanced skills and understanding in:

• computer science and programming, including the use of Python
• contemporary ethical issues and regulatory standards
• key mathematical concepts and techniques for identifying data patterns
• the role and use of state-of-the-art machine learning tools and algorithmic techniques
• critical cyber security principles and techniques
• cutting-edge modelling and event simulation methods.

You'll also combine and apply your learning in a significant workplace-based research project.

You will graduate equipped to play a leading role in meaningful and rewarding work in virtually any field you're drawn to. You could help governments target potential areas for improvement in their pandemic responses. You might enable sustainable energy providers to identify technological efficiency gains. Perhaps you'll give not-for-profits the insight they need to invest in services where the community needs them most.

• Ranked #50 in the world for computer science and engineering^
• Available to be studied either full-time, part-time or accelerated mode
• Accredited by the Australian Computer Society (ACS)

^ARWU, 2022