Skip to main content

Master of Data Science (MADS) - MDSc

CRICOS code (International applicants): 0101854

 On-campusOnline
Semester intake:Semester 1 (February)
Semester 2 (July)
Semester 1 (February)
Semester 2 (July)
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 

Back to top

Contact us

Future Australian and New Zealand students  Future International students  Current students 
Ask a question
Freecall (within Australia): 1800 269 500
Phone (from outside Australia): +61 7 4631 5315
Email: study@usq.edu.au 
Ask a question
Phone: +61 7 4631 5543
Email: international@usq.edu.au 
Ask a question
Freecall (within Australia): 1800 007 252
Phone (from outside Australia): +61 7 4631 2285
Email: usq.support@usq.edu.au  

Back to top

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.

Back to top

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.


Back to top

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 www.aqf.edu.au.

Back to top

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 English language requirements.

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

Back to top

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 student contribution amount, which varies depending on the courses undertaken. Students are able to calculate the fees for a particular course via the Course Fee Finder.

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

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 Course Fee Finder.

Domestic full fee paying students may be eligible to defer their fees through a Government loan called FEE-HELP 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 FEE-Help.

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 Course Fee Finder.

Back to top

Program structure

The Master of Data Science consists of 16 units of courses including either a research training track or a research project track.


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 one approved course.) with one or two 2-unit research project courses (MSC8001 Research Project I and MSC8002 Research Project II).

Students who have completed STA2300 Data Analysis in their Bachelor’s degree will undertake an alternative course to STA8170 Statistics for Quantitative Researchers.

Students taking the Research Project Track may take MSC8001 Research Project I and MSC8002 Research Project II OR MSC8003 Industry Based Research Practice I and MSC8004 Industry Based Research Practice II with the approval of the Program Director.

The Master of Data Science consists of 11 core courses (11 units), 1 approved course, plus 4 units of pathway courses available either on-campus or online:

Semester 1  Semester 2  Either Semester 
CIS8025 Big Data Analytics  CSC8002 Big Data Management  CSC8001 Introduction to Data Science and Visualisation 
STA8005 Multivariate Analysis for High-Dimensional Data  CSC8003 Machine Learning  1 x Approved course 
CSC8004 Data Mining  CSC8600 Advanced ICT Professional Project  CIS8008 Business Intelligence 
STA8170 Statistics for Quantitative Researchers    CIS5310 Management of Information Systems Projects 
    CSC1401 Foundation Programming 
and EITHER the following four courses, which comprise the Research Training Track: 
SCI8103 Research Fundamentals and Ethics  SCI8101 Science in Practice   
1 x Approved course   SCI8102 Research Skills   
OR the following two courses (subject to prior approval), which comprise the Research Project Track:  
MSC8001 Research Project I*  MSC8002 Research Project II*   
OR (instead of MSC8001 and MSC8002): 
MSC8003 Industry Based Research Practice I *  MSC8004 Industry Based Research Practice II *   

Footnotes
*Two-unit course.

Approved Courses

More approved courses from other disciplines may be available after consultation with the Program Director via usq.support@usq.edu.au

Semester 1  Semester 2  Semester 3 
Agriculture Discipline Approved Courses 
AGR8001 Food Security in the 21st Century  AGR8002 Emerging Technologies in Agriculture   
CLI8001 Climate Risk##  AGR8003 Critical Issues in Agriculture   
Astrophysics Discipline Approved Courses 
Compulsory initial course: 
PHY8001 Observational Astronomy*##     
Plus any one from the following:     
PHY8002 Planetary Science*##  PHY8003 Galactic Astronomy and Cosmology*##   
  PHY8004 Stellar Astronomy*##   
Business Discipline Approved Courses 
CIS8702 Crypto-currencies  MKT8011 Digital Marketing  CIS8011 Digital Innovation 
  CIS8011 Digital Innovation   
Computing Discipline Approved Courses  
CSC2402 Object-Oriented Programming in C++**  CSC2401 Algorithms and Data Structures**   
CSC2408 Software Development Tools**  CSC2404 Operating Systems**   
CSC8507 Networking Technologies**@  CSC2406 Web Technology 1**   
CSC8512 Advanced System Administration**  CSC2407 Introduction to Software Engineering**   
CSC8407 Wireless and Internet Technology **  CSC2408 Software Development Tools **   
CSC8416 Advanced Programming in Java**  CSC8527 Scaling and Connecting Networks**@   
CSC8419 Cryptography and Security**  CSC8426 Advanced Web Technology**   
CSC8503 Principles of Programming Languages**  CSC8420 Mobile Systems**   
  CSC8421 Network Security**   
  CSC8415 Computer Network Programming**   
  CSC8513 Network Performance Analysis**   
Information Systems Discipline Approved Courses 
CIS5100 Professional Skills for Information Systems  CIS5100 Professional Skills for Information Systems  CIS5100 Professional Skills for Information Systems 
CIS8500 Applied Research for Information System Professionals  CIS5101 Digital Enterprise  CIS5101 Digital Enterprise 
CIS8000 Global Information Systems Strategy  CIS5205 Management of Information Security  CIS5209 Systems Analysis for IT Professionals 
CIS5308 Management of Information Technology Services  CIS8000 Global Information Systems Strategy  CIS8011 Digital Innovation 
CIS8004 Enterprise Planning and Implementation  CIS8011 Digital Innovation   
CIS8501 Applied Information Systems Research Project  CIS8018 Cyber Security   
CIS5309 Management of Business Networks and the Cloud  CIS8500 Applied Research for Information System Professionals   
  CIS8501 Applied Information Systems Research Project   
Mathematics Discipline Approved Courses  
MAT2409 High Performance Numerical Computing**  MAT3104 Mathematical Modelling in Financial Economics**   
MAT3201 Operations Research 2**  MAT3103 Mathematical Modelling and Dynamical Systems**   
MAT8180 Mathematics/Statistics Complementary Studies A**  MAT8190 Mathematics/Statistics Complementary Studies B**   
Statistics Discipline Approved Courses  
STA2301 Distribution Theory**  STA2302 Statistical Inference**   
STA3300 Experimental Design**  STA3301 Statistical Models**   
STA8180 Advanced Statistics A**  STA8190 Advanced Statistics B**   

Footnotes
##Online offer only
*Two unit course
**Enrolment in this course is subject to having the correct prerequisites or equivalent.
@The CISCO certificate training is only available in ONC mode. Students who seek the certificate should enrol in ONC mode and be able to attend compulsory weekly workshops at Toowoomba campus.

Back to top

Required time limits

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

Back to top

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.

Back to top

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.

Back to top

Credit

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

  • 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.


Back to top

Enrolment

Recommended enrolment patterns

In this section:

Back to top

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 studied
Enrolment 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 Analytics1111Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
STA8170 Statistics for Quantitative Researchers1111Enrolment is not permitted in STA8170 if STA2300 has been previously completed.
CSC1401 Foundation Programming11,211,2,3

Year 1 Semester 2

CIS8008 Business Intelligence**12
CSC8002 Big Data Management1212Pre-requisite or Co-requisite: CSC1401 and (STA2300 or STA8170) or equivalent program and statistical knowledge and skills.
CSC8003 Machine Learning1212Pre-requisite: (STA2300 or STA8170) and CSC1401 or equivalent program and statistical knowledge and skills.
CIS5310 Management of Information Systems Projects**12Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 2 Semester 1

STA8005 Multivariate Analysis for High-Dimensional Data2121Pre-requisite or Co-requisite: STA8170 or STA2300
CSC8004 Data Mining2121Pre-requisite: (STA2300 or STA8170) and CSC1401

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.
     Approved Course^2121

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: CIS8010 or CIS5310 and Students must be enrolled in one of the following Programs: MCTN or MCOP or MSCN (Applied Data Science) or MADS
Approved Course^2222

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, 2020.
^For a comprehensive list of Approved Courses, refer to Program Structure Section.
*Two unit course

Back to top

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 studied
Enrolment requirements
On-campus
(ONC)
External
(EXT)
Online
(ONL)
YearSemYearSemYearSem

Year 1

CSC8001 Introduction to Data Science and Visualisation11,211,2
STA8170 Statistics for Quantitative Researchers1111Enrolment is not permitted in STA8170 if STA2300 has been previously completed.
CSC1401 Foundation Programming11,211,2,3
CSC8002 Big Data Management1212Pre-requisite or Co-requisite: CSC1401 and (STA2300 or STA8170) or equivalent program and statistical knowledge and skills.

Year 2

CIS8025 Big Data Analytics2121Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
CIS8008 Business Intelligence2121,2
CSC8003 Machine Learning2222Pre-requisite: (STA2300 or STA8170) and CSC1401 or equivalent program and statistical knowledge and skills.
CIS5310 Management of Information Systems Projects**22Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 3

STA8005 Multivariate Analysis for High-Dimensional Data3131Pre-requisite or Co-requisite: STA8170 or STA2300
CSC8004 Data Mining3131Pre-requisite: (STA2300 or STA8170) and CSC1401
CSC8600 Advanced ICT Professional Project3232Pre-requisite: CIS8010 or CIS5310 and Students must be enrolled in one of the following Programs: MCTN or MCOP or MSCN (Applied Data Science) or MADS
Approved Course^3232

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
     Approved Course^4242

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, 2020.
^For a comprehensive list of Approved Courses, refer to Program Structure Section.
*Two unit course

Back to top

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 studied
Enrolment requirements
On-campus
(ONC)
External
(EXT)
Online
(ONL)
YearSemYearSemYearSem

Year 1 Semester 2

CSC1401 Foundation Programming11,211,2,3
CIS8008 Business Intelligence**12
CIS5310 Management of Information Systems Projects**12Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.
CSC8001 Introduction to Data Science and Visualisation11,211,2

Year 1 Semester 1

STA8170 Statistics for Quantitative Researchers1111Enrolment is not permitted in STA8170 if STA2300 has been previously completed.
CIS8025 Big Data Analytics1111Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
CSC8600 Advanced ICT Professional Project1111Pre-requisite: CIS8010 or CIS5310 and Students must be enrolled in one of the following Programs: MCTN or MCOP or MSCN (Applied Data Science) or MADS
Approved Course^1111

Year 2 Semester 2

CSC8002 Big Data Management2222Pre-requisite or Co-requisite: CSC1401 and (STA2300 or STA8170) or equivalent program and statistical knowledge and skills.
CSC8003 Machine Learning2222Pre-requisite: (STA2300 or STA8170) and CSC1401 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 2 Semester 1

STA8005 Multivariate Analysis for High-Dimensional Data2121Pre-requisite or Co-requisite: STA8170 or STA2300
CSC8004 Data Mining2121Pre-requisite: (STA2300 or STA8170) and CSC1401

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.
     Approved Course^2121

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

     MSC8002 Research Project II*2121Pre-requisite: MSC8001

or

     MSC8004 Industry Based Research Practice II*2121Pre-requisite: MSC8003

Footnotes
**This course is offered online only in Semester 2, 2020.
^For a comprehensive list of Approved Courses, refer to Program Structure Section.
*Two unit course