Master of Data Science (Data Analytics)

What you will study

The program consists of 16 units comprising of:

• Eight units of core ICT courses
• Four units of specialisation
• Four units of elective courses

Core Units (eight units)

• Big Data Management
• Machine Learning
• Data Mining
• Foundations of Programming
• IS/ICT Project Management
• Multivariate Analysis for High-Dimensional Data
• Advanced ICT Professional Project
• Statistics for Quantitative Researchers

Specialisation (four units)

Select one of the following specialisation:

• Artificial Intelligence and Machine Learning
• Data Analytics

Electives (four units)

Select one of the following three (3) options:

• Research Dissertation
• Applied Research Skills
• Any four (4) postgraduate courses

Entry requirements

Applicants must have one of the following:

• Three-year bachelor degree from an Australian university, or equivalent, in any area .
• A minimum of five years' professional work experience equivalent to an AQF Level 7 qualification (bachelor).

There are inherent requirements that must be met in order to successfully complete this degree. Inherent requirements are fundamental skills, capabilities and knowledge that students must be able to demonstrate in order to achieve the essential learning outcomes of the degree, while maintaining the academic integrity of the degree. Please read and understand the inherent requirements specific to the Master of Data Science before applying.

If you think you may experience any problems meeting inherent requirements, you can talk to a Student Equity Officer about reasonable adjustments that may be put in place to assist you. Any reasonable adjustments made must not fundamentally change the nature of the inherent requirement.

English language requirements

To meet the English language requirements for this degree, we accept the combined level and duration of study specified below, or the minimum English language proficiency scores from one of the following tests. Your test must be completed within two years of applying to UniSQ.

• Tertiary study (Diploma level or higher): 1+ years full-time tertiary study in a country from the Accepted English-speaking countries list.
• IELTS (Academic) : Minimum overall score of 6.0 with a minimum 5.5 in each subscore (speaking, listening, writing, reading).
• Pearson Test of English: Minimum overall score of 50 with a minimum of 50 in each subscore.
• TOEFL iBT: Minimum overall score of 80 with a minimum of 19 in each subscore.

If you do not meet the English language requirements, you can apply to study the English for Academic Purposes EAP2 pathway program through the Union Institute of Language (UIL).

Recognition of Prior Learning

If you have previously studied in your home country, you may be eligible for recognition of prior learning to reduce the number of courses that you need to study to finish your program.

Outcomes

Career outcomes

• This degree provides a diverse range of career outcomes with opportunity to advance your profession into any big-data related field. As a graduate, you will become an efficient data scientist, utilising predictive analytics, big data management, big data analysis and modelling, to produce insights that make important, real-life decisions.
• Data scientists are highly sought after and the demand is set to increase as organisations, within technical, engineering, science, business, education and research sectors, require skilled employees to understand and interpret data to benefit business productivity.
• Careers may include: Data analyst (within health, marketing etc.), data scientist, data consultant, data engineer, business analyst, supply chain analyst, mining data analyst, fraud data analyst and data analytics manager.

Learning outcomes

Upon completion of this program, graduates will be able to:

• Autonomously apply key ICT and data science professional knowledge, technologies and programming skills to critically investigate and analyse contemporary core issues in a global market and to develop big data analysis and evidence-based decision-making skills.
• Select, adapt and apply specialised quantitative and technical skills to work independently and collaboratively to process and interpret major theories and concepts associated with big data to solve and interpret complex and real-life problems.
• Work under broad direction within a team environment, manage conflict and take a leadership role for a task within the project.
• Apply and communicate ethical, legal and professional standards related to big data privacy and building of a security culture and assess and evaluate risks in order to comply with customer organisational requirements.
• Investigate, critically analyse, evaluate and communicate research findings and problem solutions associated with applied data theories and methodologies to specialist and non-specialist audiences.