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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 

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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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  

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Professional accreditation

The Master of Data Science is Australian Computer Society (ACS) 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.

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 Student Academic Progress Procedure.

  • 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 www.aqf.edu.au.

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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 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.

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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 requirements 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 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 Schedules.

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 Schedule

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, external or online. Students are able to calculate the fees for a particular course via the Course Fee Schedules.

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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)


Master of Data Science (Enterprise Data Science) - 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 
STA6200 Statistics for Quantitative Researchers  1,2   
CIS8008 Business Intelligence   1,2 
CSC6001 Introduction to Data Science and Visualisation   1,2  1,2   
CSC6002 Big Data Management  2,3 
CSC6003 Machine Learning  2,3   
CSC6004 Data Mining   
STA6100 Multivariate Analysis for High-Dimensional Data   
CIS8025 Big Data Visualisation   1,2  1,2  1,2 
CIS8500 Applied Research for Information System Professionals  1,2 
CSC6200 Advanced ICT Professional Project   1,2  1,2   

Master of Data Science (Artificial Intelligence and Machine Learning Specialisation) - 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 
STA6200 Statistics for Quantitative Researchers  1,2   
CSC6201 Deep Learning*   
CSC6202 Natural Language Processing: Techniques and Applications*   
CSC6203 Intelligent Multimedia (Computer Vision, Audio Analysis)*     
CSC6204 Information Retrieval and Knowledge Management^  1,2  1,2   
CSC6002 Big Data Management  2,3 
CSC6003 Machine Learning  2,3   
CSC6004 Data Mining   
STA6100 Multivariate Analysis for High-Dimensional Data   
CSC6200 Advanced ICT Professional Project  1,2  1,2   

Footnotes
*Commencing 2024
^First offer S2 2023

Master of Data Science (Data Analytics Specialisation) - 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 
STA6200 Statistics for Quantitative Researchers  1,2   
CIS8008 Business Intelligence  1,2 
CSC8450 Relational Database Systems   
CSC6002 Big Data Management  2,3 
CSC6003 Machine Learning  2,3   
CSC6004 Data Mining   
STA6100 Multivariate Analysis for High-Dimensional Data   
CIS8711 Cloud Security   
CSC6205 *     
CSC6200 Advanced ICT Professional Project  1,2  1,2   

Footnotes
*Commencing 2024

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 
MSC6001 Research Project I*#  1,2  1,2   
MSC6002 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 
MSC6003 Industry Based Research Practice I*#  1,2   
MSC6004 Industry Based Research Practice II*#   
OR       
SCI6101 Science in Practice  1,2     
SCI6102 Research Skills  1,2     
SCI6103 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 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 UniSQ 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:

    • 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:

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

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 (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).


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

Year 1 Semester 1

CIS8025 Big Data Visualisation11,211,2Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
CSC5020 Foundations of Programming11,2,311,2,3
CSC6001 Introduction to Data Science and Visualisation11,211,2
STA6200 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed

Year 1 Semester 2

CIS5310 IS/ICT Project Management1111,2,3Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.
CIS8008 Business Intelligence1111,2
CSC6002 Big Data Management1212,3Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170 or STA6200) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS
CSC6003 Machine Learning1212,3Pre-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills or CSC8002 or STA6002 for MCYS students

Year 2 Semester 1

CSC6004 Data Mining2121Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
STA6100 Multivariate Analysis for High-Dimensional Data2121Pre-requisite or Co-requisite: STA8170 or STA6200 or STA2300 or STA1003 Enrolment is not permitted in STA6100 if STA3200 has been previously completed

Either the following two courses for the Research Training Track

     SCI6103 Research Fundamentals and Ethics21,221,2Pre-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 SCI6103 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)

     MSC6001 Research Project I*21,221,2Pre-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

     MSC6003 Industry Based Research Practice I*21,221Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 2 Semester 2

CSC6200 Advanced ICT Professional Project21,221,2Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course
CIS8500 Applied Research for Information System Professionals21,221,2Pre-requisite: CIS8001 or CIS8008

Either the following two courses for the Research Training Track

     SCI6101 Science in Practice21,2
     SCI6102 Research Skills21,2

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

     MSC6002 Research Project II*21,221,2Pre-requisite: MSC8001 or MSC6001

or

     MSC6004 Industry Based Research Practice II*2222Pre-requisite: MSC8003 or MSC6003

Footnotes
*Two unit course

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

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 (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).


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
STA6200 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed
CIS8008 Business Intelligence1112
CIS5310 IS/ICT Project Management1111,2,3Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 2 Semester 1

CSC6001 Introduction to Data Science and Visualisation21,221,2
CIS8025 Big Data Visualisation21,221,2Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
CSC6004 Data Mining2121Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
CIS8500 Applied Research for Information System Professionals21,221,2Pre-requisite: CIS8001 or CIS8008

Year 2 Semester 2

CSC6002 Big Data Management2222,3Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170 or STA6200) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS
CSC6003 Machine Learning2222,3Pre-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills or CSC8002 or STA6002 for MCYS students

Either the following two courses for the Research Training Track

     SCI6101 Science in Practice21,2
     SCI6102 Research Skills21,2

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

     MSC6001 Research Project I*21,221,2Pre-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

     MSC6003 Industry Based Research Practice I*21,221Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 3 Semester 1

STA6100 Multivariate Analysis for High-Dimensional Data3131Pre-requisite or Co-requisite: STA8170 or STA6200 or STA2300 or STA1003 Enrolment is not permitted in STA6100 if STA3200 has been previously completed
CSC6200 Advanced ICT Professional Project31,231,2Pre-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

     SCI6103 Research Fundamentals and Ethics31,231,2Pre-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 SCI6103 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)

     MSC6002 Research Project II*31,231,2Pre-requisite: MSC8001 or MSC6001

or

     MSC6004 Industry Based Research Practice II*3232Pre-requisite: MSC8003 or MSC6003

Footnotes
*Two unit course

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

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 (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).


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

Year 1

STA6200 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed
CSC5020 Foundations of Programming11,2,311,2,3
CSC6001 Introduction to Data Science and Visualisation11,211,2
CSC6002 Big Data Management1212,3Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170 or STA6200) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS

Year 2

CIS8025 Big Data Visualisation21,221,2Enrolment is not permitted in CIS8025 if CIS8701 has been previously completed.
CIS8008 Business Intelligence2121,2
CSC6003 Machine Learning2222,3Pre-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills or CSC8002 or STA6002 for MCYS students
CIS5310 IS/ICT Project Management2121,2,3Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 3

STA6100 Multivariate Analysis for High-Dimensional Data3131Pre-requisite or Co-requisite: STA8170 or STA6200 or STA2300 or STA1003 Enrolment is not permitted in STA6100 if STA3200 has been previously completed
CSC6004 Data Mining3131Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
CSC6200 Advanced ICT Professional Project31,231,2Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course
CIS8500 Applied Research for Information System Professionals31,231,2Pre-requisite: CIS8001 or CIS8008

Year 4

Either the following two courses for the Research Training Track

     SCI6103 Research Fundamentals and Ethics41,241,2Pre-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 SCI6103 if SCI4405 has been previously completed.
     SCI6101 Science in Practice41,2

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

     MSC6001 Research Project I*41,241,2Pre-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

     MSC6003 Industry Based Research Practice I*41,241Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Either the following two courses for the Research Training Track

     SCI6102 Research Skills41,2
     Elective4242

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

     MSC6002 Research Project II*41,241,2Pre-requisite: MSC8001 or MSC6001

or

     MSC6004 Industry Based Research Practice II*4242Pre-requisite: MSC8003 or MSC6003

Footnotes
*Two unit course

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

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 (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).


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

Year 1 Semester 1

STA6200 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed
CSC5020 Foundations of Programming11,2,311,2,3
CSC6004 Data Mining1111Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
STA6100 Multivariate Analysis for High-Dimensional Data1111Pre-requisite or Co-requisite: STA8170 or STA6200 or STA2300 or STA1003 Enrolment is not permitted in STA6100 if STA3200 has been previously completed

Year 1 Semester 2

CSC6204 Information Retrieval and Knowledge Management^11,211,2Pre-requisite or Co-requisite: CSC5020 and STA6200
CSC6002 Big Data Management1212,3Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170 or STA6200) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS
CSC6003 Machine Learning1212,3Pre-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills or CSC8002 or STA6002 for MCYS students
CIS5310 IS/ICT Project Management1111,2,3Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 2 Semester 1

CSC6202 Natural Language Processing: Techniques and Applications*2121Pre-requisite: CSC5020 and (STA8170 or STA6200)
CSC6201 Deep Learning*2121

Either the following two courses for the Research Training Track

     SCI6103 Research Fundamentals and Ethics21,221,2Pre-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 SCI6103 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)

     MSC6001 Research Project I*21,221,2Pre-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

     MSC6003 Industry Based Research Practice I*21,221Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 2 Semester 2

CSC6200 Advanced ICT Professional Project21,221,2Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course
CSC6203 Intelligent Multimedia (Computer Vision, Audio Analysis)*22

Either the following two courses for the Research Training Track

     SCI6101 Science in Practice21,2
     SCI6102 Research Skills21,2

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

     MSC6002 Research Project II*21,221,2Pre-requisite: MSC8001 or MSC6001

or

     MSC6004 Industry Based Research Practice II*2222Pre-requisite: MSC8003 or MSC6003

Footnotes
^First offer S2 2023
*Commencing 2024

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

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 (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).


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
STA6200 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed
CSC6002 Big Data Management1212,3Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170 or STA6200) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS
CIS5310 IS/ICT Project Management1111,2,3Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.

Year 2 Semester 1

STA6100 Multivariate Analysis for High-Dimensional Data2121Pre-requisite or Co-requisite: STA8170 or STA6200 or STA2300 or STA1003 Enrolment is not permitted in STA6100 if STA3200 has been previously completed
CSC6004 Data Mining2121Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
CSC6204 Information Retrieval and Knowledge Management^21,221,2Pre-requisite or Co-requisite: CSC5020 and STA6200
CSC6202 Natural Language Processing: Techniques and Applications*2121Pre-requisite: CSC5020 and (STA8170 or STA6200)

Year 2 Semester 2

CSC6003 Machine Learning2222,3Pre-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills or CSC8002 or STA6002 for MCYS students
CSC6203 Intelligent Multimedia (Computer Vision, Audio Analysis)*22

Either the following two courses for the Research Training Track

     SCI6101 Science in Practice21,2
     SCI6102 Research Skills21,2

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

     MSC6001 Research Project I*21,221,2Pre-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

     MSC6003 Industry Based Research Practice I*21,221Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 3 Semester 1

CSC6201 Deep Learning*3131
CSC6200 Advanced ICT Professional Project31,231,2Pre-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

     SCI6103 Research Fundamentals and Ethics31,231,2Pre-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 SCI6103 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)

     MSC6002 Research Project II*31,231,2Pre-requisite: MSC8001 or MSC6001

or

     MSC6004 Industry Based Research Practice II*3232Pre-requisite: MSC8003 or MSC6003

Footnotes
^First offer S2 2023
*Commencing 2024

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

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 (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).


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

Year 1 Semester 1

STA6100 Multivariate Analysis for High-Dimensional Data1111Pre-requisite or Co-requisite: STA8170 or STA6200 or STA2300 or STA1003 Enrolment is not permitted in STA6100 if STA3200 has been previously completed
STA6200 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed
CSC5020 Foundations of Programming11,2,311,2,3
CIS8008 Business Intelligence1111,2

Year 1 Semester 2

CIS5310 IS/ICT Project Management1111,2,3Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.
CSC6002 Big Data Management1212,3Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170 or STA6200) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS
CSC6003 Machine Learning1212,3Pre-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills or CSC8002 or STA6002 for MCYS students
CIS8711 Cloud Security^^#1212Pre-requisite: CSC8100 and CIS5100

Year 2 Semester 1

CSC6004 Data Mining2121Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
CSC8450 Relational Database Systems2121Pre-requisite: CSC5020

Either the following two courses for the Research Training Track

     SCI6103 Research Fundamentals and Ethics21,221,2Pre-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 SCI6103 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)

     MSC6001 Research Project I*21,221,2Pre-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

     MSC6003 Industry Based Research Practice I*21,221Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 2 Semester 2

CSC6200 Advanced ICT Professional Project21,221,2Pre-requisite: CIS5310 and Students must have successfully completed 12 units prior to enrolment in this course
CSC6205 ^22

Either the following two courses for the Research Training Track

     SCI6101 Science in Practice21,2
     SCI6102 Research Skills21,2

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

     MSC6002 Research Project II*21,221,2Pre-requisite: MSC8001 or MSC6001

or

     MSC6004 Industry Based Research Practice II*2222Pre-requisite: MSC8003 or MSC6003

Footnotes
^^On-campus at Springfield only
#MADS students may receive prerequisites override by the MADS Program Director
*Two unit course
^Commencing 2024

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

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 (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).


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
STA6200 Statistics for Quantitative Researchers1111,2Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed
CSC6002 Big Data Management1212,3Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170 or STA6200) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS
CIS8711 Cloud Security^^#1212Pre-requisite: CSC8100 and CIS5100

Year 2 Semester 1

CIS8008 Business Intelligence2121,2
CSC6004 Data Mining2121Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
CIS5310 IS/ICT Project Management2121,2,3Enrolment is not permitted in CIS5310 if CIS8010 has been previously completed.
STA6100 Multivariate Analysis for High-Dimensional Data2121Pre-requisite or Co-requisite: STA8170 or STA6200 or STA2300 or STA1003 Enrolment is not permitted in STA6100 if STA3200 has been previously completed

Year 2 Semester 2

CSC6205 ^22
CSC6003 Machine Learning2222,3Pre-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020) or equivalent program and statistical knowledge and skills or CSC8002 or STA6002 for MCYS students

Either the following two courses for the Research Training Track

     SCI6101 Science in Practice21,2
     SCI6102 Research Skills21,2

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

     MSC6001 Research Project I*21,221,2Pre-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

     MSC6003 Industry Based Research Practice I*21,221Pre-requisite: Students must be enrolled in one of the following programs: MSCN or MADS

Year 3 Semester 1

CSC8450 Relational Database Systems3131Pre-requisite: CSC5020
CSC6200 Advanced ICT Professional Project31,231,2Pre-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

     SCI6103 Research Fundamentals and Ethics31,231,2Pre-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 SCI6103 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)

     MSC6002 Research Project II*31,231,2Pre-requisite: MSC8001 or MSC6001

or

     MSC6004 Industry Based Research Practice II*3232Pre-requisite: MSC8003 or MSC6003

Footnotes
^^On-campus at Springfield only
#MADS students may receive prerequisites override by the MADS Program Director
^Commencing 2024
*Two unit course