Master of Data Science
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For more information on degree program requirements visit CUSP.
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition | Session |
|---|---|---|---|
Data Science |
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Master of Data Science |
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| Students complete 48 credit points, comprising: | |||
| (a) 24 credit points of Core units of study | |||
| (b) 12 credit points of Project units | |||
| (c) a maximum of 12 credit points of Data Science Elective units of study | |||
| (d) a maximum of 12 credit points of non Data Science Elective units of study | |||
| – Where a waiver is granted for a COMP core unit of study another COMP unit must be taken and where the waiver is granted for STAT5003 another STAT unit of study must be taken. | |||
Graduate Certificate in Data Science |
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| Students complete 24 credit points, comprising of the following: | |||
| (a) 18 credit points of Core units of study | |||
| (b) 6 credit points of Foundation units of study | |||
| – Where a waiver is granted for a COMP core unit of study, another COMP unit must be taken, and where the waiver is granted for STAT5002, another STAT unit of study must be taken. | |||
Master of Data Science |
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Core |
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| 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) |
Semester 1 Semester 2 |
| 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 |
Semester 1 Semester 2 |
| COMP5318 Machine Learning and Data Mining |
6 | A INFO2110 OR ISYS2110 OR COMP9120 OR COMP5138 |
Semester 1 Semester 2 |
| STAT5003 Computational Statistical Methods |
6 | A STAT5002 or equivalent introductory statistics course with a statistical computing component Note: Department permission required for enrolment |
Semester 1 Semester 2 |
Project |
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| The Project can be completed either as the two 6 credit point units, DATA5707 and DATA5708, over two semesters, or as the 12 credit point unit, DATA5703, in one semester. | |||
| DATA5703 Data Science Capstone Project |
12 | P A candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit N DATA5707 or DATA5708 or DATA5709 |
Semester 1 Semester 2 |
| DATA5707 Data Science Capstone A |
6 | P A part-time enrolled candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit N DATA5703 or DATA5709. Eligible students of the Data Science Capstone Project may choose either DATA5703 or (DATA5707 and DATA5708) or DATA5709 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
| DATA5708 Data Science Capstone B |
6 | P A part-time enrolled candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit C DATA5707 N DATA5703 or DATA5709. Eligible students of the Data Science Capstone Project may choose either DATA5703 or (DATA5707 and DATA5708) or DATA5709 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
| DATA5709 Data Science Capstone Project - Individual |
12 | P A candidate for the MDS who has completed 24 credit points from Core or Elective units of study, and has a WAM of 75+ may take this unit N DATA5703 or DATA5707 or DATA5708 Note: Department permission required for enrolment Students are required to source for a project and an academic supervisor prior to enrolment. |
Semester 1 Semester 2 |
Data Science Electives |
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| Complete a maximum of 12 credit points from the following: | |||
| COMP5046 Natural Language Processing |
6 | A Knowledge of an OO programming language |
Semester 1 |
| COMP5328 Advanced Machine Learning |
6 | C COMP5318 OR COMP3308 OR COMP3608 |
Semester 2 |
| COMP5329 Deep Learning |
6 | A COMP5318 |
Semester 1 |
| 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) |
Semester 2 |
| COMP5349 Cloud Computing |
6 | A Basic knowledge of computer networks as covered in INFO1112 or COMP9201 or COMP9601 (or equivalent UoS from different institutions) |
Semester 1 |
| 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) |
Semester 1 |
| 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) |
Intensive July |
| INFO5301 Information Security Management |
6 | A This unit of study assumes foundational knowledge of Information systems management. Two year IT industry exposure and a breadth of IT experience will be preferable |
Semester 1 |
| QBUS6810 Statistical Learning and Data Mining |
6 | P (ECMT5001 or QBUS5001 or STAT5003) and (BUSS6002 or COMP5310 or COMP5318) Students should complete BUSS6002 before enrolling in this unit as QBUS6810 builds on the material covered in BUSS6002. |
Semester 1 Semester 2 |
| QBUS6840 Predictive Analytics |
6 | P (QBUS5001 or ECMT5001 or STAT5003) and (BUSS6002 or COMP5310 or COMP5318) |
Semester 1 Semester 2 |
Non-Data Science Electives |
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| Complete a maximum of 12 credit points from the following:. | |||
| CSYS5010 Introduction to Complex Systems |
6 | Semester 1 Semester 2 |
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| DATA5207 Data Analysis in the Social Sciences |
6 |
Note: Department permission required for enrolment in the following sessions:Intensive December |
Intensive December Semester 1 |
| EDPC5012 Evaluating Learning Tech. Innovation |
6 | Semester 1 |
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| EDPC5025 Learning Technology Research Frontiers |
6 | Semester 2 |
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| HTIN5005 Applied Healthcare Data Science |
6 | Semester 2 |
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| ITLS6107 Applied GIS and Spatial Data Analytics This unit of study is not available in 2022 |
6 | N TPTM6180 This unit assumes no prior knowledge of GIS; the unit is hands-on involving the use of software, which students will be trained in using. |
Semester 2 |
| PHYS5033 Environmental Footprints and IO Analysis |
6 |
Minimum class size of 5 students. |
Semester 1 Semester 2 |
Graduate Certificate in Data Science |
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Core |
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| 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 |
Semester 1 Semester 2 |
| 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 |
Semester 1 Semester 2 |
| STAT5002 Introduction to Statistics |
6 | A HSC Mathematics |
Semester 1 Semester 2 |
Foundation Units |
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| COMP9001 Introduction to Programming |
6 | N INFO1110 OR INFO1910 OR INFO1103 OR INFO1903 OR INFO1105 OR INFO1905 OR ENGG1810 |
Semester 1 Semester 2 |
| COMP9007 Algorithms This unit of study is not available in 2022 |
6 | A This unit of study assumes that students have general knowledge of mathematics (especially Discrete Math) and problem solving. Having moderate knowledge about Data structures can also help students to better understand the concepts of Algorithms taught in this course. N COMP5211 |
Semester 1 Semester 2 |
| COMP9123 Data Structures and Algorithms |
6 | N INFO1105 OR INFO1905 OR COMP2123 OR COMP2823 |
Semester 1 Semester 2 |