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Master of Data Science (MADS) - MDSc

CRICOS code (International applicants): 0101854

 On-campusOnline
Start:Semester 1 (February)
Semester 2 (July)
Semester 1 (February)
Semester 2 (July)
Semester 3 (November)
Campus:Toowoomba -
Fees:Commonwealth supported place
Domestic full fee paying place
International full fee paying place
Commonwealth supported place
Domestic full fee paying place
International full fee paying place
Standard duration:2 years full-time, 4 years part-time 

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Contact us

Future Australian and New Zealand students  Future International students  Current students 

Freecall (within Australia): 1800 269 500
Phone (from outside Australia): +61 7 4631 5315
Email: study@usq.edu.au 

Phone: +61 7 4631 5543
Email: international@usq.edu.au 

Freecall (within Australia): 1800 007 252
Phone (from outside Australia): +61 7 4631 2285
Email: usq.support@usq.edu.au  

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Professional accreditation

The Master of Data Science is Australian Computer Society () accredited, giving eligibility 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.

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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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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, via distance education/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

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


Core ICT courses

Courses  Semester of offer Online  Semester of offer Toowoomba campus  Semester of offer Springfield campus 
CSC5020 Foundations of Programming  1,2,3  1,2,3   
CIS5310 IS/ICT Project Management  1,2,3 
STA8170 Statistics for Quantitative Researchers   
CIS8008 Business Intelligence   1,2 
CSC8001 Introduction to Data Science and Visualisation   1,2  1,2   
CSC8002 Big Data Management  2,3 
CSC8003 Machine Learning  2,3   
CSC8004 Data Mining   
STA8005 Multivariate Analysis for High-Dimensional Data*   
CIS8025 Big Data Visualisation   1,2  1,2   
CIS8500 Applied Research for Information System Professionals  1,2 
CSC8600 Advanced ICT Professional Project   1,2  1,2   

Footnotes
*Unavailable in on-campus mode in 2022

Research

Research dissertation courses as electives

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

Courses  Online  Toowoomba  Springfield 
MSC8001 Research Project I*#  1,2  1,2   
MSC8002 Research Project II*#  1,2  1,2   

Footnotes
*Two-unit course
#Subject to prior approval by Program Director

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.

Courses  Online  Toowoomba  Springfield 
MSC8003 Industry Based Research Practice I*#  1,2   
MSC8004 Industry Based Research Practice II*#   
OR       
SCI8101 Science in Practice  1,2     
SCI8102 Research Skills  1,2     
SCI8103 Research Fundamentals and Ethics  1,2  1,2   
1 x Elective course       

Footnotes
*Two-unit course
#Subject to prior approval by Program Director

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Required time limits

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

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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 Science (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:

    • USQ'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:

    • USQ'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:

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

Students are able to enrol in any offered mode of a course (on-campus, external or online), regardless of the program mode of study they enrolled in.

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 (SCI8101 Science in Practice, SCI8102 Research Skills , SCI8103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC8001 Research Project I and MSC8002 Research Project II) or (MSC8003 Industry Based Research Practice I and MSC8004 Industry Based Research Practice II ).


CourseYear of program and semester in which course is normally studiedEnrolment requirements
On-campus
(ONC)
External
(EXT)
Online
(ONL)
YearSemYearSemYearSem

Year 1 Semester 1

CSC8001 Introduction to Data Science and Visualisation11,211,2
CIS8025 Big Data Visualisation1111Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
STA8170 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA8170 if STA2300 or STA1003 has been previously completed.
CSC5020 Foundations of Programming11,2,311,2,3

Year 1 Semester 2

CIS8008 Business Intelligence**12
CSC8002 Big Data Management1212Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS.
CSC8003 Machine Learning1212Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills
CIS5310 IS/ICT Project Management**12Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 2 Semester 1

STA8005 Multivariate Analysis for High-Dimensional Data#2121Pre-requisite or Co-requisite: STA8170 or STA2300 or STA1003
CSC8004 Data Mining2121Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020)

Either the following two courses for the Research Training Track

     SCI8103 Research Fundamentals and Ethics2121Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MSCR or MCTN or MADS or GCSC or GDSI or DPHD or its equivalent. Enrolment is not permitted in SCI8103 if SCI4405 has been previously completed.
     Elective2121

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8001 Research Project I*2121Pre-requisite: Students must be enrolled in one of the following Programs: MCTN or MCOP or MCTE or MSCN or MCCO or MADS or have the approval of their program coordinator

or

     MSC8003 Industry Based Research Practice I*2121Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 2 Semester 2

CSC8600 Advanced ICT Professional Project2222Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course
CIS8500 Applied Research for Information System Professionals2222Pre-requisite: CIS8001 or CIS8008

Either the following two courses for the Research Training Track

     SCI8101 Science in Practice21,2
     SCI8102 Research Skills21,2

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8002 Research Project II*2222Pre-requisite: MSC8001

or

     MSC8004 Industry Based Research Practice II*2222Pre-requisite: MSC8003

Footnotes
**This course is offered online only in Semester 2.
#Unavailable in on-campus mode in 2022
*Two unit course

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

Students are able to enrol in any offered mode of a course (on-campus, external or online), regardless of the program mode of study they enrolled in.

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 (SCI8101 Science in Practice, SCI8102 Research Skills, SCI8103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC8001 Research Project I and MSC8002 Research Project II) or (MSC8003 Industry Based Research Practice I and MSC8004 Industry Based Research Practice II).


CourseYear of program and semester in which course is normally studiedEnrolment requirements
On-campus
(ONC)
External
(EXT)
Online
(ONL)
YearSemYearSemYearSem

Year 1

CSC8001 Introduction to Data Science and Visualisation11,211,2
STA8170 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA8170 if STA2300 or STA1003 has been previously completed.
CSC5020 Foundations of Programming11,2,311,2,3
CSC8002 Big Data Management1212Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS.

Year 2

CIS8025 Big Data Visualisation2121Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
CIS8008 Business Intelligence2121,2
CSC8003 Machine Learning2222Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills
CIS5310 IS/ICT Project Management**22Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 3

STA8005 Multivariate Analysis for High-Dimensional Data#3131Pre-requisite or Co-requisite: STA8170 or STA2300 or STA1003
CSC8004 Data Mining3131Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020)
CSC8600 Advanced ICT Professional Project3232Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course
CIS8500 Applied Research for Information System Professionals3232Pre-requisite: CIS8001 or CIS8008

Year 4

Either the following two courses for the Research Training Track

     SCI8103 Research Fundamentals and Ethics4141Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MSCR or MCTN or MADS or GCSC or GDSI or DPHD or its equivalent. Enrolment is not permitted in SCI8103 if SCI4405 has been previously completed.
     SCI8101 Science in Practice41,2

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8001 Research Project I*4141Pre-requisite: Students must be enrolled in one of the following Programs: MCTN or MCOP or MCTE or MSCN or MCCO or MADS or have the approval of their program coordinator

or

     MSC8003 Industry Based Research Practice I*4141Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Either the following two courses for the Research Training Track

     SCI8102 Research Skills41,2
     Elective4242

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8002 Research Project II*4242Pre-requisite: MSC8001

or

     MSC8004 Industry Based Research Practice II*4242Pre-requisite: MSC8003

Footnotes
**This course is only offered online in S2.
#Unavailable in on-campus mode in 2022
*Two unit course

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

Students are able to enrol in any offered mode of a course (on-campus, external or online), regardless of the program mode of study they enrolled in.

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 (SCI8101 Science in Practice, SCI8102 Research Skills , SCI8103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC8001 Research Project I and MSC8002 Research Project II) or (MSC8003 Industry Based Research Practice I and MSC8004 Industry Based Research Practice II).


CourseYear of program and semester in which course is normally studiedEnrolment requirements
On-campus
(ONC)
External
(EXT)
Online
(ONL)
YearSemYearSemYearSem

Year 1 Semester 2

CSC5020 Foundations of Programming11,2,311,2,3
CIS8008 Business Intelligence**12
CIS5310 IS/ICT Project Management**12Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.
STA8170 Statistics for Quantitative Researchers12Enrolment is not permitted in STA8170 if STA2300 or STA1003 has been previously completed.

Year 2 Semester 1

CSC8001 Introduction to Data Science and Visualisation21,221,2
CIS8025 Big Data Visualisation2121Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
CSC8004 Data Mining2121Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020)
CIS8500 Applied Research for Information System Professionals2121Pre-requisite: CIS8001 or CIS8008

Year 2 Semester 2

CSC8002 Big Data Management2222Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS.
CSC8003 Machine Learning2222Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills

Either the following two courses for the Research Training Track

     SCI8101 Science in Practice21,2
     SCI8102 Research Skills21,2

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8001 Research Project I*2222Pre-requisite: Students must be enrolled in one of the following Programs: MCTN or MCOP or MCTE or MSCN or MCCO or MADS or have the approval of their program coordinator

or

     MSC8003 Industry Based Research Practice I*22Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 3 Semester 1

STA8005 Multivariate Analysis for High-Dimensional Data#3131Pre-requisite or Co-requisite: STA8170 or STA2300 or STA1003
CSC8600 Advanced ICT Professional Project3131Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course

Either the following two courses for the Research Training Track

     SCI8103 Research Fundamentals and Ethics3131Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MSCR or MCTN or MADS or GCSC or GDSI or DPHD or its equivalent. Enrolment is not permitted in SCI8103 if SCI4405 has been previously completed.
     Elective3131

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8002 Research Project II*3131Pre-requisite: MSC8001

or

     MSC8004 Industry Based Research Practice II*3232Pre-requisite: MSC8003

Footnotes
**This course is offered online only in Semester 2.
*Two unit course
#Unavailable in on-campus mode in 2022

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Recommended Enrolment Pattern - Full-time (S3 entry)

Students are able to enrol in any offered mode of a course (on-campus, external or online), regardless of the program mode of study they enrolled in.

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 (SCI8101 Science in Practice, SCI8102 Research Skills, SCI8103 Research Fundamentals and Ethics and/or 1 approved course) with one or two 2-unit research project courses (MSC8001 Research Project I and MSC8002 Research Project II) or (MSC8003 Industry Based Research Practice I and MSC8004 Industry Based Research Practice II).

International students may not be able to enrol in 4 courses for a full time workload in Semester 3.


CourseYear of program and semester in which course is normally studiedEnrolment requirements
On-campus
(ONC)
External
(EXT)
Online
(ONL)
YearSemYearSemYearSem

Year 1 Semester 3

CSC5020 Foundations of Programming1313
CIS5310 IS/ICT Project Management13Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 2 Semester 1

CSC8001 Introduction to Data Science and Visualisation2121
CIS8025 Big Data Visualisation2121Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
STA8170 Statistics for Quantitative Researchers2121Enrolment is not permitted in STA8170 if STA2300 or STA1003 has been previously completed.
CIS8008 Business Intelligence2121

Year 2 Semester 2

CSC8600 Advanced ICT Professional Project2222Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course
CSC8002 Big Data Management2222Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS.
CSC8003 Machine Learning2222Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills
CIS8500 Applied Research for Information System Professionals2121Pre-requisite: CIS8001 or CIS8008

Year 3 Semester 1

CSC8004 Data Mining3131Pre-requisite: (STA2300 or STA1003 or STA8170) and (CSC1401 or CSC5020)
STA8005 Multivariate Analysis for High-Dimensional Data#3131Pre-requisite or Co-requisite: STA8170 or STA2300 or STA1003

Either the following two courses for the Research Training Track

     SCI8101 Science in Practice31
     SCI8102 Research Skills31

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8001 Research Project I*3131Pre-requisite: Students must be enrolled in one of the following Programs: MCTN or MCOP or MCTE or MSCN or MCCO or MADS or have the approval of their program coordinator

or

     MSC8003 Industry Based Research Practice I*3131Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 2 Semester 2

Either the following two courses for the Research Training Track

     SCI8103 Research Fundamentals and Ethics3232Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MSCR or MCTN or MADS or GCSC or GDSI or DPHD or its equivalent. Enrolment is not permitted in SCI8103 if SCI4405 has been previously completed.
     Elective3232

or one of the following courses for the Research Project Track (if approved instead of Research Training Track)

     MSC8002 Research Project II*3232Pre-requisite: MSC8001

or

     MSC8004 Industry Based Research Practice II*3232Pre-requisite: MSC8003

Footnotes
#Unavailable in on-campus mode in 2022
*Two unit course