Master of Data Science

Entry Requirements

Applicants to the Master of Data Science will normally have:

A Bachelor degree in Information Technology, or in Mathematics from a recognised higher education institution; OR
A Graduate Certificate in Data Science or Graduate Diploma in Data Science or equivalent from a recognised higher education institution.

English Language Requirements:

IELTS score of 6.5 (with 6.0 in Reading and Writing); TOEFL iBT score of 79 with Reading and Writing not less than 18; TOEFL paper-based test (PBT) score of 577 with TWE of 4.5; Cambridge CAE/CPE score of 177; Pearson's test of English (Academic) (PTE) score of 58 with Reading and Writing communicative scores not less than 50; CELUSA score of AE5.

Course Details

UniSA’s Master of Data Science gives you current knowledge of data science techniques and research. It caters for students with a mathematics or an IT background, with courses tailored for both.

You will learn to analyse and visualise rich data sources, how to spot data trends, and to generate data management strategies. The coursework has been designed with industry including the Institute of Analytics Professionals of Australia and the leader in business analytics software – SAS.

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

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