Master of Data Science
Online unit of study table
| This page was first published on 21 November 2022 and was last amended on 30 January 2023. 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 as two 6 credit point units over two semesters; and |
| (iii) a minimum of 18 credit points of Specialisation units of study or Data Science Specialist units of study |
| (iv) a maximum of 12 credit points of 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 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 OCMP5310 and OSTA5003; 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 |
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Core units of study |
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Data Science Core units of study |
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| OCMP5048 Visual Analytics |
6 | A Experience with data structures and algorithms as covered in COMP9123 or COMP9103 or COMP9003 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) N COMP5048 or COMP4448 |
| OCMP5310 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 COMP5310 or INFO3406 |
| OSTA5003 Computational Statistical Methods |
6 | A STAT5002 or equivalent introductory statistics course with a statistical computing component |
Professional Core units of study |
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| OINF5990 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 INFO5990 |
| OINF5992 Understanding IT Innovations |
6 | N INFO5992 or PMGT5875 |
Data Science Specialist units of study |
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| OCMP5318 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) N COMP5318 or COMP4318 |
| OCMP5328 Advanced Machine Learning |
6 | C OCMP5318 or COMP5318 or COMP4318 or COMP3308 or COMP3608 N COMP5328 or COMP4328 |
| OCMP5329 Deep Learning |
6 | A OCMP5318 or COMP5318 or COMP4318 N COMP5329 or COMP4329 |
| OCMP5338 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 COMP5338 or COMP4338 |
| OCMP5339 Data Engineering |
6 | A Proficiency in programming, especially Python, and in database querying with SQL; basic Unix scripting P COMP5310 or OCMP5310 N COMP5329 or COMP4329 |
Elective units of study |
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| OCMP5426 Parallel and Distributed Computing |
6 | A Experience with algorithm design and software development as covered in (COMP2017 or COMP9017) and COMP3027 or COMP3927 (or equivalent UoS from different institutions) N COMP5426 or COMP4426 |
Capstone Project units of study |
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| Data Science Capstone A |
6 | Unit under development. Not available for enrolment in 2023. |
| Data Science Capstone B |
6 | Unit under development. Not available for enrolment in 2023. |
Specialisations for the Master of Data Science |
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| A Specialisation requires the completion of 18 credit points of Specialisation Core units of study as defined in the tables below. |
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Data Engineering specialisation |
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| Specialisation Core units of study | ||
| OCMP5338 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 COMP5338 or COMP4338 |
| OCMP5349 Cloud Computing |
6 | A Basic knowledge of computer networks as covered in INFO1112 or COMP9201 or COMP9601 (or equivalent UoS from different institutions) N COMP5349 or COMP4349 |
| OCMP5339 Data Engineering |
6 | A Proficiency in programming, especially Python, and in database querying with SQL; basic Unix scripting P COMP5310 or OCMP5310 N COMP5329 or COMP4329 |
Machine Learning specialisation |
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| Specialisation Core units of study | ||
| OCMP5318 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) N COMP5318 or COMP4318 |
| OCMP5328 Advanced Machine Learning |
6 | C OCMP5318 or COMP5318 or COMP4318 or COMP3308 or COMP3608 N COMP5328 or COMP4328 |
| OCMP5329 Deep Learning |
6 | A OCMP5318 or COMP5318 or COMP4318 N COMP5329 or COMP4329 |
Unspecified specialisation |
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| Unspecified Specialisation requires the completion of 18 credit points from the Data Science Specialist units of study table. | ||
