Master of Data Science
Online unit of study table
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. |
Completion of Electives is optional and unit availability is limited. Students are encouraged to select from the Data Science Specialist units of study options to fulfill course requirements.
Units of study listed in this table are only available to students enrolled in a Postgraduate Online degree. |
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| 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 A good understanding of statistics, including hypothesis testing and regression modelling, and substantial statistical computing experience. For example, both ODAT5011 and ODAT5021 or a unit like STAT5002. |
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 |
| OINF5995 Introduction to Cybersecurity |
6 | N INFO5995 This unit of study is being developed, subject to change Unit rules are prospective only |
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). Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra and calculus (e.g. MATH1061 or equivalent) 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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| ODAT5707 Data Science Capstone A |
6 | A A Master of Data Science (online) candidate who has completed 24 credit points of Data Science Core units of study or Data Science Specialist units of study or Specialisation Core units of study may take this unit P 24 credit points of (OCMP5048 or OCMP5310 or OSTA5003 or OCMP5318 or OCMP5328 or COMP5329 or OCMP5338 or OCMP5339 or OCMP5349) N DATA5702 or DATA5703 or DATA5704 or DATA5707 or DATA5708 or DATA5709 or COMP5802 |
| ODAT5708 Data Science Capstone B |
6 | A A Master of Data Science (online) candidate who has completed 24 credit points of Data Science Core units of study or Data Science Specialist units of study or Specialisation Core units of study may take this unit P 24 credit points of (OCMP5048 or OCMP5310 or OSTA5003 or OCMP5318 or OCMP5328 or COMP5329 or OCMP5338 or OCMP5339 or OCMP5349) C ODAT5707 N DATA5702 or DATA5703 or DATA5704 or DATA5707 or DATA5708 or DATA5709 or COMP5802 |
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 |
| 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 |
| 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 |
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). Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra and calculus (e.g. MATH1061 or equivalent) 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. | ||
