Graduate Certificate in Data Science

Entry requirements

Duration
0.5 YEAR(S) FULL-TIME
Scholarship
NO
Fee
AUD$ 19,250 PER ANNUM (
Intake
FEBRUARY JULY

To be eligible for admission to the Graduate Certificate in Data Science, an applicant must:

hold a Bachelor degree in Information Technology, or in Mathematics from a recognised higher education institution OR

have a minimum of three years professional experience.

Applicants with a Bachelor degree from fields other than Information Technology or Mathematics, plus a minimum of 1 years professional experience, are also encouraged to apply and will be assessed on a case by case basis.

English language entry requirements

In addition to meeting academic entry requirements, international students who speak English as a second or additional language must also meet the University's English language entry requirements. The minimum language requirements for this program are:

English language test

IELTS total [6.5]

IELTS reading [6.0]

IELTS writing [6.0]

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Degree overview

Study at the No.1 university in SA for I.T. facilities1 and learning resources in I.T.2.

Enter the revolutionary area of big data where there is an increase in demand for data science skills3.

Learn to analyse and visualise rich data sources, how to spot data trends, and to generate data management strategies.

Complete coursework designed with industry including the Institute of Analytics Professionals of Australia and one of the leaders in business analytics software – SAS.

Career outcomes

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. geo-spatial); 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 analyst: 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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