Master of Data Science

Why study the Master of Data Science at UNE?

Data is a vital asset valued by virtually every organisation in the world. It provides the basis for sensible, evidence-based decision making, and the ability to manage and make sense of data is a key skill in the modern workplace. In the last decade, streams of data of various types have grown in volume and velocity such that they require specialist skills in order to get the most value from them. Data scientists are responsible for buildng intelligent systems, mastering intuitive proesses and bringing structure to the vast quantities of data to unlock the potential for improvement and competitive advantage.

The Master of Data Science will provide students with the required knowledge and practical skills to analyse and manage data. They will learn how to deal with large and diverse data sets and apply a variety of technologies to extract meaning from them. This course will prepare students to solve complex and challenging problems in science, health, business and beyond, through a combination of engaging coursework subjects and a comprehensive capstone project experience.

The Master of Data Science is an entry-level postgraduate course that complements any existing skill set and provides opportunity to up-skill for positions that have a rigorous quantitative aspect. Graduates from this program will be well-equipped to tackle complex data science challenges and play a leading role in the future development of data science solutions globally.

The Master of Data Science has been granted Professional Level accreditation by the Australian Computer Society and, through the Seoul Accord, is recognised in other countries.

Career Opportunities

Data science is a rapidly growing field with outstanding employment prospects for appropriately qualified professionals. Potential positions include: data scientist, business intelligence analyst, data engineer, data architect, data strategist, healthcare data managers, bioinformatics analyst, computational scientist and research scientists.

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Degree Snapshot

DURATION

1 or 1.5 or 2 Years Full-time
Up to 6 years Part-time

FEES

CSP (quotas apply)
Full Fee
International

2018 STUDY OPTIONS
Armidale

Trimester 1, Online
Trimester 1, On Campus
Trimester 2, Online
Trimester 2, On Campus
Trimester 3, Online

Official Abbreviation MDataSc
Course Type Postgraduate
CRICOS Code 096381K
Commencing
Responsible Campus Admission Period Mode of Study
Armidale Trimester 1 Online
Armidale Trimester 1 On Campus
Armidale Trimester 2 Online
Armidale Trimester 2 On Campus
Armidale Trimester 3 Online
Course Duration
  • 1 or 1.5 or 2 Years Full-time
  • Up to 6 years Part-time
Fees CSP (quotas apply) / Full Fee / International
Total Credit Points 96
How to Apply

All domestic students apply through the link below

For more information, click here

International students apply direct to UNE through UNE International

For more information, click here

Entry Requirements

A candidate shall:

(a) hold an AQF Level 7 Bachelor qualification in a non-relevant discipline; or

(b) hold an AQF Level 7 Bachelor qualification in a relevant discipline*; or

(c) hold an AQF Level 8 Graduate Certificate or Graduate Diploma or Bachelor with Honours qualification in a relevant discipline*.

*Relevant disciplines include, but are not limited to:

Computer Science

Data Science

Information Systems

Information Technology

Mathematics

Software Engineering

Statistics

Additional Requirements

Inherent Requirements: Students must meet the Inherent Requirements in order to complete this course.

Advanced Standing

Candidates are referred to the University Policy on Advanced Standing.

Candidates admitted under Rule (a) may be granted a maximum of 48 credit points of Advanced Standing on the basis of units that were not part of the degree on which admission was based. 6 credit points may be included on the basis of considerable professional experience.

Candidates admitted under Rule (b) shall be granted a maximum of 24 credit points of Block Advanced Standing based on their admission to candidature. A further 24 credit points of Advanced Standing may be granted on the basis of units that were not part of the degree on which admission was based. 6 credit points may be included on the basis of considerable professional experience.

Candidates admitted under Rule (c) shall be granted a maximum of 48 credit points of Block Advanced Standing based on their admission to candidature.

No Advanced Standing will be granted for COSC591 or SCI501.

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Further Information

You can find instant answers to many of your questions or contact UNE directly via AskUNE

These course rules & plans are ONLY to be used if you commenced, transferred or changed versions in the Master of Data Science in 2018.

Admission to Candidature

A candidate shall:
(a) hold an AQF Level 7 Bachelor qualification in a non-relevant discipline; or
(b) hold an AQF Level 7 Bachelor qualification in a relevant discipline*; or
(c) hold an AQF Level 8 Graduate Certificate or Graduate Diploma or Bachelor with Honours qualification in a relevant discipline*.

*Relevant disciplines include, but are not limited to:

Computer Science
Data Science
Information Systems
Information Technology
Mathematics
Software Engineering
Statistics

Additional Requirements

Inherent Requirements

Students must meet the Inherent Requirements in order to complete this course.

Advanced Standing

Candidates are referred to the University Policy on Advanced Standing.

Candidates admitted under Rule (a) may be granted a maximum of 48 credit points of Advanced Standing based on units that were not part of the degree on which admission was based. 6 credit points may be included on the basis of considerable professional experience.

Candidates admitted under Rule (b) shall be granted a maximum of 24 credit points Block Advanced Standing based on their admission to candidature. Up to a further 24 credit points of Advanced Standing may be granted on the basis of units that were not part of the degree on which admission was based. 6 credit points may be included on the basis of considerable professional experience.

Candidates admitted under Rule (c) shall be granted a maximum of 48 credit points Block Advanced Standing based on their admission to candidature.

No advanced standing will be granted for COSC591 or SCI501.

Period of Candidature

For candidates admitted under Rule (a), the period of candidature shall be:
(a) two years as a full-time candidate;
(b) up to six years as a part-time candidate.

For candidates admitted under Rule (b), the period of candidature shall be:
(a) one and a half years as a full-time candidate;
(b) up to six years as a part-time candidate.

For candidates admitted under Rule (c), the period of candidature shall be:
(a) one year as a full-time candidate;
(b) up to four years as a part-time candidate.

Course Requirements

1. To qualify for the award a candidate admitted under Rule (a) must pass units to the value of 96 credit points including not more than 18 credit points at 100-level, not more than 12 credit points at 200/300-level and at least 36 credit points at 500-level.

2. To qualify for the award a candidate admitted under Rule (b) or Rule (c) must pass units to the value of 96 credit points including at least 36 credit points at 500-level.

Program of Study

Candidates shall complete an approved program of study comprising:

For candidates admitted under Rule (a)
Course Structure Credit Points
Core Units 66 cps
Capstone Experience 12 cps
Listed Units 18 cps
Total 96 cps

To view complete Program of Study click here

For candidates admitted under Rule (b)
Course Structure Credit Points
Block Advanced Standing 24 cps
Core Units 36 cps
Capstone Experience 12 cps
Listed Units 24 cps
Total 96 cps

To view complete Program of Study click here

For candidates admitted under Rule (c)
Course Structure Credit Points
Block Advanced Standing 48 cps
Core Units 24 cps
Capstone Experience 12 cps
Listed Units 12 cps
Total 96 cps

To view complete Program of Study click here

Award of Degree

Candidates who meet the course requirements shall be awarded the Master of Data Science.

Exit Pathways

Subject to meeting Advanced Standing rules, candidates admitted under Rule (a) who apply to discontinue their studies in the Master of Data Science may be eligible to exit with the Graduate Diploma in Data Science upon successful completion of COSC110, COSC210, STAT100, SCI410 and 24 credit points at 400-level or above.

Subject to meeting Advanced Standing rules, candidates admitted under Rule (b) who apply to discontinue their studies in the Master of Data Science may be eligible to exit with the Graduate Certificate in Data Science upon successful completion of 24 credit points with at least 18 credit points at 400-level or above from the Listed Units for that course.

Candidates who apply to discontinue their studies and exit with the Graduate Diploma in Data Science or the Graduate Certificate in Data Science must apply for re-admission and will be subject to current course requirements of the Master of Data Science. This may mean they will not receive full recognition for their previous studies should the course structure have changed in response to University requirements.

Appeals

Candidates are referred to the Academic Assessment Appeals Policy and the Academic Assessment Appeals Procedures.

Course Progression

Candidates are referred to the Course Progression Rule and the Course Progression Procedures.

Improper Conduct

Candidates are referred to the Student Coursework Academic Misconduct Rule and the Student Coursework Academic Misconduct Procedures.

Course Aims

The Master of Data Science is designed for graduates from all disciplines to complement their existing skills with a solid background in statistics and computer science, preparing them for advanced subjects in these areas along with discipline-specific data science units. The degree provides access to a back-bone of statistics and computer science units, provided within the course.

Learning Outcomes Upon completion of this course, students will be able to:
  1. understand the key tools, methods and theories used in data science to a level of depth and sophistication consistent with advanced professional practice;
  2. synthesise information from data and analyse the lifecycle of data within an organisation;
  3. apply problem solving skills and advanced knowledge to implement data analysis solutions for real-world problems;
  4. communicate effectively with expert and non-expert audiences to understand issues and gather requirements for the development of data analysis strategies and related systems within an organisation;
  5. integrate theories and methods related to data science, statistics and software development by planning and executing a research-grounded industry project;
  6. evaluate information from a range of sources, such as peer-reviewed literature and technical documentation, to assess current developments in the area of data science; and
  7. demonstrate a sophisticated awareness of the ethical and legal issues that relate to the practice of data science.
Graduate Attributes
Knowledge of a Discipline

Graduates will understand scientific practice and have sophisticated knowledge of the methods, theories and technologies in data science.

Communication Skills

Graduates will be able to effectively communicate complex ideas and issues to a variety of audiences in order to implement data analysis solutions and present scientific outcomes. Communication skills are developed through a combination of class participation, independent learning and the completion of a capstone project.

Problem Solving

Graduates will be able to apply specialist knowledge in areas of data science, statistics and software development to solve challenging problems. Problem solving is embedded throughout the course and rigorously assessed through coursework and open-ended projects.

Information Literacy

Graduates will be able to evaluate information from a range of sources, including peer-reviewed literature and technical documentation, to develop their insight within the data science discipline. This key attribute is developed through class participation, individual assessment and the execution of project work.

Ethical Conduct and Social Responsibility

Graduates will have a deep understanding of the ethical responsibilities related to the practice of data science. They will be able to assess the impact of their actions on the community and behave in a socially responsible manner in a professional environment. This awareness is developed incrementally within the course to give graduates an appreciation of the ethical and social issues that surround the different aspects of data science.

Lifelong Learning

Data Science is a very rapidly expanding field of study. Graduates have the fundamental skills which enable them to supplement their knowledge and adapt to the changes in methodology and technologies. This is taught and practised by providing core skills and exposing students to a variety of technologies, environments and specialised systems.

Independence and Collaboration

Graduates will have the ability to operate individually with a high level of autonomy and effectively as part of a team. These skills are developed through research-based projects, individual assessment and class participation.

How to Apply

Domestic Students

All domestic students apply through the link below

For more information, click here

International Students

International students apply direct to UNE through UNE International

For more information, click here

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