Master of Data Science

A course tailored to you. 

• You may enter the Master of Data Science program with a background in computer science, statistics, or both. For those without this background, you can opt to learn the foundational skills in Data Science, equipping yourself with the technological and analytical abilities needed to manage and gain insights from large and complex datasets. The program is tailored to develop your expertise and prepare you for a career in data analytics.

Overview

• The Harvard Business Review has labelled data science the "sexiest job of the 21st century".
• If they meant that jobs in data science are increasing dramatically, that data scientists can work in fields as diverse as health, retail or ecology, and that data scientists are commanding high salaries, then they were spot on.
• After all, we're creating more than 2.5 exabytes of data every day. Someone needs to make sense of it all.

Flexibility to follow your interests

• Foundation and core subjects will give you a solid grounding in data science, so you’ll have the technological and analytical abilities that are vital for managing and interpreting large, complex collections of data.
• Beyond the core subjects, elective subjects give you the freedom to dive deeper into a specialist area of data science.

Sharpen your skills with the capstone project

• You’ll leave the course with a major data science project to feature in your CV. In the capstone project, you’ll apply data science tools to a practical problem, working individually or as part of a team to showcase your skills.

More than just technical skills

• We know that you’ll need more than technical capability to succeed in the workplace. To round out your skill set, you can choose from professional skills subjects, such as scientific communication, so you can start your data science career with confidence.
• If you’d like to gain more real-world experience, you can choose to complete an internship in a science or technology-related workplace for course credit.

Course structure
All students must complete:

• 75 points of Core subjects
• 25 points of Capstone subjects
• 100 points of a formal Specialisation

Career outcomes

• Our graduates go on to work as data scientists and analysts, software engineers, data infrastructure engineers, business intelligence analysts and statisticians.

Employers in this field include:

• Consulting firms such as EY, KPMG and Accenture
• Financial services companies including Citibank, ANZ, CBA and NAB
• IT and telecommunication companies such as IBM, Microsoft and Telstra
• Government departments and organisations, such as the Australian Bureau of Statistics
• Universities and public research institutions such as the CSIRO.

Technical and professional skills

• On graduating from the course, you will have a sound knowledge of modern statistical methodology and computing that will equip you for a career in data science and enable your career to develop as data science evolves.

Graduates will:

• Demonstrate a detailed technical understanding of the key advanced tools and methods used in data science;
• Demonstrate expertise in machine learning methods and strategies for advanced data mining, expertise in database systems, statistical methodology and computational statistics;
• Integrate and apply this expertise to produce solutions for real-world problems using public and private data sources;
• Communicate findings from analyses clearly and effectively, including to an audience with a diverse background in science and/or industry;
• Demonstrate a sophisticated awareness of ethical implications relevant to the use of data, and particularly “big data”;
• Demonstrate a fundamental understanding of theoretical underpinnings of algorithms in computer science and machine learning;
• Demonstrate skills in the evaluation and synthesis of information from a range of sources and the ability to apply these skills to understand the international peer-reviewed scientific literature and primary research in data science and disciplines relevant to data science;
• Have the ability to adapt to the different domains of application and to a rapidly evolving field.

Specialisation outcomes
Foundational Data Science Specialisation Learning Outcomes

• Demonstrate knowledge of the statistical methods underpinning Data Science.
• Demonstrate knowledge of the computational methods underpinning Data Science.

Statistical Data Science Specialisation Learning Outcomes

• Demonstrate proficiency over established and emerging advanced mathematical and statistical methods in Data Science;
• Demonstrate detailed technical skills in statistical learning and inference, for analysis of complex datasets.

Computational Data Science Specialisation Learning Outcomes

• Demonstrate skills in the design, implementation, and evaluations of end-to-end data science pipelines when processing complex text and/or vision data sources;
• Demonstrate proficiency over established and emerging computational data science methods and tools to solve practical problems;

Computational and Statistical Data Science Specialisation Learning Outcomes

• Demonstrate skills in the design, implementation, and evaluations of end-to-end data science pipelines when processing complex text and/or vision data sources;
• Demonstrate proficiency over established and emerging computational data science methods and tools to solve practical problems;
• Demonstrate proficiency over established and emerging advanced mathematical and statistical methods in Data Science;
• Demonstrate detailed technical skills in statistical learning and inference, for analysis of complex datasets.

Further study

• At the end of the course, if you complete the optional research project, you could qualify to undertake a PhD (Doctor of Philosophy).