Graduate Certificate in Data Science

Entry Requirements

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 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

Take the first step on your career journey towards the revolutionary field of big data.

There’s currently high demand for data scientists1, and this graduate certificate will prepare you for the workplace, or to study towards a master degree.

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.

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 finish this graduate certificate and want to do further study, consider going on to the Graduate Diploma in Data Science or the Master of Data Science.

What you'll learn

In the Graduate Certificate in Data Science you will gain an understanding of core concepts in information technology and statistics. You will develop:

cognitive skills to review, analyse, consolidate and synthesise knowledge and identify and provide solutions to complex problems in data science
cognitive skills to think critically and to generate and evaluate complex ideas
specialised technical and creative skills in data science
communication skills to demonstrate an understanding of theoretical concepts
communication skills to transfer complex knowledge and ideas to a variety of audiences

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