Master of Data Science
For more information on degree program requirements visit CUSP https://cusp.sydney.edu.au
| 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: COMP5310, STAT5003, COMP5318, COMP5048 | |||
| (b) 12 credit points of Project units | |||
| (c) 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: | |||
| Core units of study: COMP5310, STAT5002, COMP9007, COMP9120 | |||
| – 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. | |||
Units of study |
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Master of Data Science |
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Core |
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| COMP5048 Visual Analytics |
6 | A It is assumed that students will have experience with data structure and algorithms as covered in COMP9103 OR COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 (or equivalent UoS from different institutions). Note: Department permission required for enrolment in the following sessions:Semester 1 |
Semester 1 Semester 2 |
| COMP5310 Principles of Data Science |
6 | A It is assumed that students will have 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 | P STAT5002 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
| The prerequisite for STAT5003 is waived for Master of Data Science students. Please apply for special permission for this unit of study. | |||
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. Eligible students of the Data Science Capstone Project may choose either DATA5703 or DATA5707/DATA5708. 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. Eligible students of the Data Science Capstone Project may choose either DATA5703 or DATA5707/DATA5708. 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 |
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 Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA |
Semester 1 |
| COMP5425 Multimedia Retrieval |
6 | A It is assumed that students will have experience with programming skills, as learned in COMP9103 OR COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 (or equivalent UoS from different institutions). |
Semester 1 |
| INFO5060 Data Analytics and Business Intelligence |
6 | A It is assumed that students will have the basic knowledge of information systems, which are covered in COMP5206 or ISYS2160 (or equivalent UoS from different institutions). Note: Department permission required for enrolment |
Intensive January 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 BUSS6002 |
Semester 1 Semester 2 |
| QBUS6840 Predictive Analytics |
6 | P (QBUS5001 or ECMT5001) and BUSS6002 |
Semester 1 Semester 2 |
| The prerequisites for QBUS6810 and QBUS6840 are waived for Master of Data Science students. Please apply for special permission for these units. | |||
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 | A COMP5310 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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| ITLS6107 Applied GIS and Spatial Data Analytics |
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 It is assumed that students will have 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 |
| COMP9007 Algorithms |
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 |
| 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 |