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CRICOS code (International applicants): 0101854

You are currently viewing the 2023 Handbook. For study in 2024, please refer to the .

Please be advised that this program will be transitioning from Semester to Trimester study periods in 2024. Trimester 1 starts on 22 January 2024. Read more in our new academic calendar FAQs.

Notes

In 2023 the program follows the Semester calendar. The Academic Calendar and Important Dates webpage will allow you to view and download a copy of the important dates for the Semester calendar.

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Professional accreditation

Provisional accreditation of the Master of Data Science with the Australian Computer Society () is pending. Once provisional accreditation is approved, students will be eligible for ACS membership and recognition by ACS for certification.

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Program aims

With the popularity of social media and the wide spread use of the Internet, enormous amounts of data of various types are generated at all times. The Master of Data Science is designed to provide an opportunity for graduates from all disciplines to gain advanced skills and knowledge in handling data more commonly known as Big Data, as well as producing and interpreting data analytics. The aim of this program is to provide students with a career path in Data Science and an opportunity for advancement in their career.

Program Rules

Students are required to:

• Satisfactorily complete 16 credit points as listed in the standard progression to graduate from the program.

• Satisfactorily complete all courses within 6 years.

• Maintain satisfactory academic achievement throughout the duration of the program, consistent with the UniSQ .

• Meet the Inherent Requirements for the Master of Data Science.

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Program objectives

On completion of the program students should 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.

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Australian Qualifications Framework

The Australian Qualifications Framework (AQF) is a single national, comprehensive system of qualifications offered by higher education institutions (including universities), vocational education and training institutions and secondary schools. Each AQF qualification has a set of descriptors which define the type and complexity of knowledge, skills and application of knowledge and skills that a graduate who has been awarded that qualification has attained, and the typical volume of learning associated with that qualification type.

This program is at AQF Qualification Level 09. Graduates at this level will have specialised knowledge and skills for research, and/or professional practice and/or further learning.

The full set of levels criteria and qualification type descriptors can be found by visiting .

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Admission requirements

To be eligible for admission, applicants must satisfy the following requirements:

• Completion of an Australian university three year Bachelor degree in any area, or equivalent OR

• A minimum of five years’ professional work experience equivalent to a qualification at AQF Level 7.

• English Language Proficiency requirements for Category 2.

All students are required to satisfy the applicable .

If students do not meet the English language requirements they may apply to study a ¾«¶«´«Ã½app-approved . On successful completion of the English language program, students may be admitted to an award program.

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Inherent requirements

There are inherent requirements for this program that must be met in order to complete the program and graduate. Make sure you read and understand the for this program online.

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Program fees

Commonwealth supported place

A Commonwealth supported place is where the Australian Government makes a contribution towards the cost of a students' higher education and students pay a , which varies depending on the courses undertaken. Students are able to calculate the fees for a particular course via the .

Commonwealth Supported students may be eligible to defer their fees through a Government loan called .

Domestic full fee paying place

Domestic full fee paying places are funded entirely through the full fees paid by the student. Full fees vary depending on the courses that are taken. Students are able to calculate the fees for a particular course via the

Domestic full fee paying students may be eligible to defer their fees through a Government loan called provided they meet the residency and citizenship requirements.

Australian citizens, Permanent Humanitarian Visa holders, Permanent Resident visa holders and New Zealand citizens who will be resident outside Australia for the duration of their program pay full tuition fees and are not eligible for .

International full fee paying place

International students pay full fees. Full fees vary depending on the courses that are taken and whether they are studied on-campus, external or online. Students are able to calculate the fees for a particular course via the .

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Program structure

The program consists of 16 units comprising of:

• 12 units of core ICT courses; or

• 12 units of core ICT courses for the Artificial Intelligence and Machine Learning specialisation; or

• 12 units of core ICT courses for the Data Analytics specialisation

• And either: 4 units of Research course; or

• 4 units of Research Training; or

• 4 units of elective courses (any Postgraduate courses, subject to pre-requisite satisfaction)

Research

Research dissertation courses as electives

Students wishing to pursue a PhD are encouraged to complete the research dissertation courses below as their electives.

Research training courses as electives

Students wishing to pursue a research and development career are encouraged to complete the research training courses below as their elective.

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Required time limits

Students have a maximum of six years to complete this program.

Specific specialisations

In this section:

• Enterprise Data Science
• Artificial Intelligence and Machine Learning Specialisation
• Data Analytics Specialisation

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Enterprise Data Science

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Artificial Intelligence and Machine Learning Specialisation

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Data Analytics Specialisation

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IT requirements

For information technology requirements please refer to the .

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Articulation

Students completing the research project track within the Master of Data Science would be eligible to apply for articulation to the Master of Research or Doctor of Philosophy programs if they meet other requirements for entry into those programs. Students completing the research training track within the Master of Data Science with the appropriate GPA would be eligible to apply for enrolment in the Master of Science (Research) (Advanced) and then could progress (articulate) to a Doctor of Philosophy via that route once they have demonstrated satisfactory progress in a significant research component.

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Exit points

Students may exit with the Graduate Diploma of Science (Applied Data Science) on successful completion of at least eight courses within the Master of Data Science if they have satisfied the requirements of a Graduate Diploma of Science (Applied Data Science). Students may exit with the Graduate Diploma of Science (General) if they have completed at least eight courses from the Master of Data Science, including four post-graduate courses coded at 5000 level or above.

Students may exit with the Graduate Certificate of Science (Applied Data Science) on successful completion of at least four courses within the Master of Data Science if they have satisfied the requirements of a GCSC Graduate Certificate of Science (Applied Data Science). Students may exit with the Graduate Certificate of Science (General) if they have completed at least four courses from the Master of Data Science, including at least two courses coded at 5000 level or above.

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Credit

Exemptions/credit for all specialisations will be assessed according to .

• Up to four units of coursework exemptions or credit will be granted if the student has completed courses equivalent to courses offered in the Master of Data Science in either:

  • UniSQ's Graduate Certificate of Science; or

  • A Graduate Diploma or Bachelor’s Honours Degree qualification in a discipline different from the current area of study.

  
  

• Up to eight units of coursework credit or exemptions will be granted if the student has completed courses equivalent to courses offered in the Master of Data Science in either:

  • UniSQ's Graduate Diploma of Science; or

  • A Graduate Diploma or Bachelor’s Honours Degree qualification in a discipline equivalent to the current area of study.

  
  

Notes:

1. All requests for credits or exemptions need to be sought by the student and approved by the Program Director.

2. The Program Director will deem to what extent prior studies are equivalent.

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Enrolment

Recommended enrolment patterns

In this section:

• Recommended Enrolment Pattern - Full-time (4 Semesters, S1 entry) - Enterprise Data Science
• Recommended Enrolment Pattern - Full-time (4 Semesters, S2 entry) - Enterprise Data Science
• Recommended Enrolment Pattern - Part-time (8 Semesters, S1 entry) - Enterprise Data Science
• Recommended Enrolment Pattern - Full-time (4 Semesters, S1 entry) - Artificial Intelligence and Machine Learning
• Recommended Enrolment Pattern - Full-time (4 Semesters, S2 entry) - Artificial Intelligence and Machine Learning
• Recommended Enrolment Pattern - Full-time (4 Semesters, S1 entry) - Data Analytics Specialisation
• Recommended Enrolment Pattern - Full-time (4 Semesters, S2 entry) - Data Analytics Specialisation

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Recommended Enrolment Pattern - Full-time (4 Semesters, S1 entry) - Enterprise Data Science

Students may, with approval of the Program Director and acceptance by an appropriate supervisor, elect to replace two or four units of research training courses (SCI6101 Science in Practice, SCI6102 Research Skills, SCI6103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC6001 Research Project I and MSC6002 Research Project II) or (MSC6003 Industry Based Research Practice I and MSC6004 Industry Based Research Practice II).

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Recommended Enrolment Pattern - Full-time (4 Semesters, S2 entry) - Enterprise Data Science

Students may, with approval of the Program Director and acceptance by an appropriate supervisor, elect to replace two or four units of research training courses (SCI6101 Science in Practice, SCI6102 Research Skills, SCI6103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC6001 Research Project I and MSC6002 Research Project II) or (MSC6003 Industry Based Research Practice I and MSC6004 Industry Based Research Practice II).

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Recommended Enrolment Pattern - Part-time (8 Semesters, S1 entry) - Enterprise Data Science

Students may, with approval of the Program Director and acceptance by an appropriate supervisor, elect to replace two or four units of research training courses (SCI6101 Science in Practice, SCI6102 Research Skills, SCI6103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC6001 Research Project I and MSC6002 Research Project II) or (MSC6003 Industry Based Research Practice I and MSC6004 Industry Based Research Practice II).

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Recommended Enrolment Pattern - Full-time (4 Semesters, S1 entry) - Artificial Intelligence and Machine Learning

Students may, with approval of the Program Director and acceptance by an appropriate supervisor, elect to replace two or four units of research training courses (SCI6101 Science in Practice, SCI6102 Research Skills, SCI6103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC6001 Research Project I and MSC6002 Research Project II) or (MSC6003 Industry Based Research Practice I and MSC6004 Industry Based Research Practice II).

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Recommended Enrolment Pattern - Full-time (4 Semesters, S2 entry) - Artificial Intelligence and Machine Learning

Students may, with approval of the Program Director and acceptance by an appropriate supervisor, elect to replace two or four units of research training courses (SCI6101 Science in Practice, SCI6102 Research Skills, SCI6103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC6001 Research Project I and MSC6002 Research Project II) or (MSC6003 Industry Based Research Practice I and MSC6004 Industry Based Research Practice II).

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Recommended Enrolment Pattern - Full-time (4 Semesters, S1 entry) - Data Analytics Specialisation

Students may, with approval of the Program Director and acceptance by an appropriate supervisor, elect to replace two or four units of research training courses (SCI6101 Science in Practice, SCI6102 Research Skills, SCI6103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC6001 Research Project I and MSC6002 Research Project II) or (MSC6003 Industry Based Research Practice I and MSC6004 Industry Based Research Practice II).

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Recommended Enrolment Pattern - Full-time (4 Semesters, S2 entry) - Data Analytics Specialisation

Students may, with approval of the Program Director and acceptance by an appropriate supervisor, elect to replace two or four units of research training courses (SCI6101 Science in Practice, SCI6102 Research Skills, SCI6103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC6001 Research Project I and MSC6002 Research Project II) or (MSC6003 Industry Based Research Practice I and MSC6004 Industry Based Research Practice II).