Enter the revolutionary area of big data where there is an acute shortage of data scientists. This is expected to drive future growth in the data science workforce, with an annual growth rate of 2.4% between 2016-17 and 2021-22 (which is stronger than the 1.5% per annum growth forecast for the entire Australian labour force).
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 insights based on data.
This Data Science degree from University of South Australia 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.
Your career
The field of data science field is evolving at a rapid rate. It will continue to grow as savvy business leaders integrate analytics into every facet of their organisation. Analytics, science, data, and reasoning are becoming embedded into decision-making processes, every day and everywhere in the business world1.
Careers to consider:
- 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
- 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
- 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
- data engineer: managing data workflows, pipelines, and ETL processes, preparing 'big data' infrastructure, working with data scientists and analysts
- machine learning analysts: building and implementing machine learning models, developing production software through systems in big data production pipeline, working with recommendation systems, developing customer analytics solutions
