Master of Data Science
Unit of study table
| This page was first published on 13 November 2025 and was last amended on 15 July 2026. View details of the changes below. |
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Master of Data Science |
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| To qualify for the award of the Master of Data Science, a candidate must complete 72 credit points, comprising: |
For the Professional Pathway: |
| (i) 30 credit points of Core units of study consisting of 18 credit points of Data Science Core units of study and 12 credit points of Professional Core units of study; and |
| (ii) 12 credit points of Capstone Project units of study taken either as two 6 credit point units, DATA5707 and DATA5708, over two semesters, or as a 12 credit point unit, DATA5703 or DATA5709 in one semester; and |
| (iii) a minimum of 18 credit points of Specialisation units of study or Data Science Specialist units of study; and |
| (iv) a maximum of 12 credit points of Elective or Foundation units of study. |
For the Research Pathway:
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| (i) 30 credit points of Core units of study; and |
| (ii) 24 credit points of Research Pathway units of study; and |
| (iii) 18 credit points of Specialisation units of study or Data Science Specialist units of study |
| (iv) no credit points from the Foundation or Elective units of study |
Graduate Diploma in Data Science |
| To qualify for the award of the Graduate Diploma in Data Science, a candidate must complete 48 credit points of units of study including |
| (i) A minimum of 12 credit points of Data Science Core units of study; and |
| (ii) A minimum of 6 credit points of Professional Core units of study; and |
| (iii) A minimum of 12 credit points of Data Science Specialist units of study; and |
| (iv) A maximum of 12 credit points of Foundation or Elective units of study. |
Graduate Certificate in Data Science |
| To qualify for the award of the Graduate Certificate in Data Science candidates must complete 24 credit points of units of study including: |
| (i) 12 credit points of Data Science Core units of study consisting of COMP5310 and STAT5003; and |
| (ii) 12 credit points of Data Science Specialist units of study. |
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition |
|---|---|---|
Core units of study | ||
Data Science core units of study | ||
| COMP5048 Visual Analytics | 6 | A Experience with data structures and algorithms as covered in COMP9103 or COMP9003 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) N COMP4448 or OCMP5048 |
| COMP5310 Principles of Data Science | 6 | A Good understanding of relational data model and database technologies as covered in ISYS2120 or COMP9120 (or equivalent UoS from different institutions) N INFO3406 or OCMP5310 |
| STAT5003 Computational Statistical Methods | 6 | A STAT5002 or equivalent introductory statistics course with a statistical computing component |
Professional core units of study | ||
| INFO5990 Professional Practice in IT | 6 | A Students enrolled in INFO5990 are assumed to have previously completed a Bachelor's degree in some area of IT, or have completed a Graduate Diploma in some area of IT, or have many years experience as a practising IT professional. N INFO1111 or OINF5990 The main focus of the subject is to provide students with the necessary tools, basic skills, experience and adequate knowledge so they develop an awareness and an understanding of the responsibilities and issues associated with professional conduct and practice in the information technology sector |
| INFO5995 Introduction to Cybersecurity | 6 | N OINF5995 or INFO5992 or OINF5992 |
Data Science Specialist units of study | ||
| COMP5046 Natural Language Processing | 6 | A Knowledge of an OO programming language N COMP4446 |
| COMP5318 Machine Learning and Data Mining | 6 | A Experience with programming and data structures as covered in COMP2123 or COMP2823 or COMP9123 (or equivalent unit of study from different institutions). Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra and calculus (e.g. MATH1061 or equivalent) N COMP4318 or OCMP5318 |
| COMP5328 Advanced Machine Learning | 6 | C COMP5318 or COMP4318 or COMP3308 or COMP3608 N COMP4328 or OCMP5328 |
| COMP5329 Deep Learning | 6 | A COMP4318 or COMP5318 N COMP4329 or OCMP5329 |
| COMP5338 Advanced Data Models | 6 | A This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1) N COMP4338 or OCMP5338 |
| COMP5339 Data Engineering | 6 | A Proficiency in programming, especially Python, and in database querying with SQL; basic Unix scripting P COMP5310 N OCMP5339 |
| COMP5349 Cloud Computing | 6 | A Basic programming skills as covered in INFO1110 or INFO1910 or ENGG1810 or COMP9001 or COMP9003. Knowledge of OS concepts as covered in INFO1112 or COMP9201 or COMP9601 would be an advantage. N COMP4349 or OCMP5349 |
| INFO5060 Data Analytics and Business Intelligence | 6 | A Basic knowledge of information systems as covered in COMP5206 or ISYS2160 (or equivalent UoS from different institutions) |
Foundation units of study | ||
| COMP9001 Introduction to Programming | 6 | N INFO1110 or INFO1910 or INFO1103 or INFO1903 or INFO1105 or INFO1905 or ENGG1810 |
| COMP9017 Systems Programming | 6 | A COMP9003; discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra (e.g. MATH1061 or equivalent) N COMP2129 or COMP2017 or COMP9129 |
| COMP9110 System Analysis and Modelling | 6 | A Experience with a data model as in COMP9129 or COMP9103 or COMP9003 or COMP9220 or COMP9120 or COMP5212 or COMP5214 or COMP5028 or COMP5138 N ELEC3610 or ELEC5743 or INFO2110 or INFO5001 or ISYS2110 |
| COMP9120 Database Management Systems | 6 | A Some exposure to programming and some familiarity with data model concepts N INFO2120 or INFO2820 or INFO2005 or INFO2905 or COMP5138 or ISYS2120 Students who have previously studied an introductory database subject as part of their undergraduate degree should not enrol in this foundational unit as it covers the same foundational content |
| COMP9121 Design of Networks and Distributed Systems | 6 | N COMP5116 |
| COMP9123 Data Structures and Algorithms | 6 | A Discrete mathematics and probability (e.g. MATH1064 or equivalent) and programming experience (e.g. INFO1110 or COMP9001 or equivalent) N INFO1105 or INFO1905 or COMP2123 or COMP2823 |
| INFO6007 Project Management in IT | 6 | A Students enrolled in INFO6007 are assumed to have previously completed a Bachelor's degree in some area of IT, or have completed a Graduate Diploma in some area of IT, or have three years experience as a practising IT professional. Recent work experience, or recent postgraduate education, in software project management, software process improvement, or software quality assurance is an advantage. N PMGT5871 or INFO3333 |
| STAT5002 Introduction to Statistics | 6 | A HSC Mathematics |
Elective units of study | ||
| COMP5047 Pervasive Computing | 6 | A ELEC1601 and (COMP2129 or COMP2017 or COMP9017). Background in programming and operating systems that is sufficient for the student to independently learn new programming tools from standard online technical materials N COMP4447 |
| COMP5216 Mobile Computing | 6 | A COMP5214 or COMP9103 or COMP9003. Software Development in JAVA, or similar introductory software development units N COMP4216 |
| COMP5313 Large Scale Networks | 6 | A Algorithmic skills gained through units such as COMP2123 or COMP2823 or COMP3027 or COMP3927 or COMP9007 or COMP9123 or equivalent. Basic probability knowledge N COMP4313 |
| COMP5347 Web Application Development | 6 | A Experience with software development as covered in SOFT2412 or COMP9412 or INFO1113 or COMP9103 or COMP9003 and experience in database management systems as covered in ISYS2120 or COMP9120. N COMP4347 |
| COMP5348 Enterprise Scale Software Architecture | 6 | A Experience with software development as covered in SOFT2412 or COMP9103 and also COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) N COMP4348 |
| COMP5416 Advanced Network Technologies | 6 | A COMP3221 or ELEC3506 or ELEC9506 or ELEC5740 or COMP5116 or COMP9121 N COMP4416 |
| COMP5425 Multimedia Retrieval | 6 | A Experience with programming skills, as covered in COMP9103 or COMP9003 or COMP9123 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) N COMP4425 |
| COMP5426 Parallel and Distributed Computing | 6 | A Experience with algorithm design and software development as covered in (COMP2017 or COMP9017) and COMP3027 (or equivalent UoS from different institutions) N COMP4426 or OCMP5426 |
| COMP5427 Usability Engineering | 6 | N COMP4427 |
| CSYS5010 Introduction to Complex Systems | 6 | |
| CSYS5030 Information Theory and Self-Organisation | 6 | A Competency in 1st year mathematics, and basic computer programming skills are assumed. Competency in 1st year undergraduate level statistics (for example, covering probabilities, conditional probabilities, Gaussian distribution, correlations, statistical significance/hypothesis testing and p-values). An exposure to linear algebra would be useful but not mandatory |
| DATA5207 Data Analysis in the Social Sciences | 6 | N DATA4207 |
| ELEC5304 Intelligent Visual Signal Understanding | 6 | A Mathematics (e.g. probability and linear algebra) and programming skills (e.g. Matlab/Java/Python/C++) |
| ELEC5305 Acoustics, Speech and Signal Processing | 6 | A (ELEC2302 or ELEC9302) and (ELEC3305 or ELEC9305). Linear algebra, fundamental concepts of signals and systems as covered in ELEC2302/ELEC9302, fundamental concepts of digital signal processing as covered in ELEC3305/9305. It would be unwise to attempt this unit without the assumed knowledge- if you are not sure, please contact the instructor |
| ELEC5306 Video Intelligence and Compression | 6 | A Basic understanding of digital signal processing (filtering, DFT) and programming skills (e.g. Matlab/Java/Python/C++) |
| ELEC5514 IoT Wireless Sensing and Networking | 6 | A ELEC3305 and ELEC3506 and ELEC3607 and ELEC5508 |
| ELEC5517 Software Defined Networks | 6 | A ELEC3506 or ELEC9506 |
| ELEC5618 Software Quality Engineering | 6 | A Writing programs with multiple functions or methods in multiple files; design of complex data structures and combination in non trivial algorithms; use of an integrated development environment; software version control systems |
| PHYS5033 Environmental Footprints and IO Analysis | 6 | |
Capstone Project units of study | ||
| DATA5703 Data Science Capstone Project | 12 | A A candidate of [Master of Data Science (2022 and prior) who has completed 24 credit points from (Data Science Core or Data Science Elective) units of study] or [Master of Data Science (2023 onwards) who has completed 36 credit points] may take this unit. P 24 credit points from (COMP5046 or COMP5048 or COMP5310 or COMP5313 or COMP5318 or COMP5328 or COMP5329 or COMP5338 or COMP5339 or COMP5349 or COMP5425 or INFO5060 or QBUS6810 or QBUS6840 or STAT5003) N DATA5702 or DATA5704 or DATA5707 or DATA5708 or DATA5709 or ODAT5707 or ODAT5708 or COMP5802 |
| DATA5707 Data Science Capstone A | 6 | A A part time candidate of [Master of Data Science (2022 and prior) who has completed 24 credit points from (Data Science Core or Data Science Elective) units of study] or [Master of Data Science (2023 onwards) who has completed 36 credit points] may take this unit. P A part-time enrolled candidate for the MDS who has completed 24 credit points from (COMP5046 or COMP5048 or COMP5310 or COMP5313 or COMP5318 or COMP5328 or COMP5329 or COMP5338 or COMP5339 or COMP5349 or COMP5425 or INFO5060 or QBUS6810 or QBUS6840 or STAT5003) N DATA5702 or DATA5703 or DATA5704 or DATA5709 or ODAT5707 or ODAT5708 or COMP5802 |
| DATA5708 Data Science Capstone B | 6 | A A part time candidate of [Master of Data Science (2022 and prior) who has completed 24 credit points from (Data Science Core or Data Science Elective) units of study] or [Master of Data Science (2023 onwards) who has completed 36 credit points] may take this unit. P A part-time enrolled candidate for the MDS who has completed 24 credit points from (COMP5046 or COMP5048 or COMP5310 or COMP5313 or COMP5318 or COMP5328 or COMP5329 or COMP5338 or COMP5339 or COMP5349 or COMP5425 or INFO5060 or QBUS6810 or QBUS6840 or STAT5003) C DATA5707 N DATA5702 or DATA5703 or DATA5704 or DATA5709 or ODAT5707 or ODAT5708 or COMP5802 |
| DATA5709 Data Science Capstone Project - Individual | 12 | A A candidate of [Master of Data Science (2022 and prior) who has completed 24 credit points from (Data Science Core or Data Science Elective) units of study] or [Master of Data Science (2023 onwards) who has completed 36 credit points] who has a WAM of 75 or more may take this unit. P A candidate for the MDS who has a WAM of 75+ and has completed 24 credit points from (COMP5046 or COMP5048 or COMP5310 or COMP5313 or COMP5318 or COMP5328 or COMP5329 or COMP5338 or COMP5339 or COMP5349 or COMP5425 or INFO5060 or QBUS6810 or QBUS6840 or STAT5003) N DATA5702 or DATA5704 or DATA5703 or DATA5707 or DATA5708 or ODAT5707 or ODAT5708 or COMP5802 |
Research Pathway units of study | ||
| DATA5702 Data Science Research Project A | 12 | A 12 credit points of Data Science Core and 12 credit points of (Specialisation Core or Data Science Specialist) units of study with a WAM of 75 or above N DATA5703 or DATA5707 or DATA5708 or DATA5709 or ODAT5707 or ODAT5708 or COMP5802 |
| DATA5704 Data Science Research Project B | 6 | A 12 credit points of Data Science Core and 12 credit points of (Specialisation Core or Data Science Specialist) units of study with a WAM of 75 or above N DATA5703 or DATA5707 or DATA5708 or DATA5709 or ODAT5707 or ODAT5708 or COMP5802 |
| INFO5993 Computer Science Research Methods | 6 | N INFO4990 |
Specialisations for the Master of Data Science | ||
| A Specialisation requires the completion of 18 credit points of Specialisation Core units of study as defined in the tables below. | ||
Data Engineering specialisation | ||
| Specialisation core units of study | ||
| COMP5338 Advanced Data Models | 6 | A This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1) N COMP4338 or OCMP5338 |
| COMP5339 Data Engineering | 6 | A Proficiency in programming, especially Python, and in database querying with SQL; basic Unix scripting P COMP5310 N OCMP5339 |
| COMP5349 Cloud Computing | 6 | A Basic programming skills as covered in INFO1110 or INFO1910 or ENGG1810 or COMP9001 or COMP9003. Knowledge of OS concepts as covered in INFO1112 or COMP9201 or COMP9601 would be an advantage. N COMP4349 or OCMP5349 |
Machine Learning specialisation | ||
| Specialisation core units of study | ||
| COMP5318 Machine Learning and Data Mining | 6 | A Experience with programming and data structures as covered in COMP2123 or COMP2823 or COMP9123 (or equivalent unit of study from different institutions). Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra and calculus (e.g. MATH1061 or equivalent) N COMP4318 or OCMP5318 |
| COMP5328 Advanced Machine Learning | 6 | C COMP5318 or COMP4318 or COMP3308 or COMP3608 N COMP4328 or OCMP5328 |
| COMP5329 Deep Learning | 6 | A COMP4318 or COMP5318 N COMP4329 or OCMP5329 |
Unspecified specialisation | ||
| Unspecified Specialisation requires the completion of 18 credit points from the Data Science Specialist units of study table. | ||
Post-publication amendments
| Date | Original publication | Post-publication amendment |
|---|---|---|
| 15/07/2026 | Prohibition (N) for INFO5995 published as: "N OINF5995" | Prohibition (N) for INFO5995 amended to: "N OINF5995 or INFO5992 or OINF5992" |