Enter the revolutionary area of big data where there is an acute shortage of data scientists. A recent McKinsey Global Institute report projects a 50 per cent gap between projected demand and supply by 20181.

Vast volumes of data are generated every day around the globe. The need to make sense of it has given rise to the revolutionary area of ‘Big Data’, and to a new career of ‘data scientist’. Data scientists find patterns, making meaning and drawing value from the seeming chaos.

Taught by leading researchers you will learn to analyse and visualise rich data sources, how to spot data trends and to generate data management strategies.

This postgraduate degree is offered as part of a suite of three programs (graduate certificate, graduate diploma and master). Each qualification extends to the next, so you can easily transition to a master level qualification.

If you decide to exit this degree having completed the first four courses you will receive the Graduate Certificate in Data Science. If you have completed the first eight courses you will receive the Graduate Diploma in Data Science.

1McKinsey Global Institute. Big Data: The next frontier for innovation, competition and productivity, May 2011.

What you'll learn

You will start by developing foundation skills in data and statistics such as big data basics, statistical programming for data science, and relational databases and warehouses. You will then go on to study data analytics courses such as:

• Predictive Analytics
• Unsupervised Methods in Analytics
• Research Methods
• Data Visualisation

You’ll finish your degree with a professional project where you’ll work in a structured project team, getting practical experience in modern data science techniques and practices.

• big data visualiser: using visualisation software to analyse data, drawing implications and communicating findings; providing input on database requirements for reporting/analytics; acquiring, managing and documenting data (e.g. geospatial); creating visualisations from data or GIS data analysis
• data scientist: understanding interfaces, data migrations, big data and databases; taking the lead in processing raw data and determining the best types of analysis; mining large volumes of data to understand user behaviours and interactions; communicating data findings to IT leadership and business leaders to promote innovation
• business data analyst: working with stakeholders, analysts and senior management to understand business strategy and the questions that need to be asked; identifying research needs; designing experiments and making recommendations based on results; driving complex analytics projects to support the business
• information security analyst: reporting and recommendations to prevent security incidents; security control monitoring; implementing new security technology, methods and techniques; championing security best practice; reviewing systems for security risks and compliance issues