Unit of study table
Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition | Session |
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Master of Data Science |
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Candidates for the degree of Master of Data Science are required to complete 48 credit points from the units of study listed in the tables below as follows: | |||
1. 24 credit points of Core units of study including: COMP5310, COMP5318, COMP5048, STAT5003 | |||
2. 12 credit points of Project units of study | |||
3. a maximum of 12 credit points of non-Data Science Elective units of study as approved by the Academic Director | |||
To qualify for the Graduate Certificate in Data Science, candidates must complete the following core units: | |||
COMP5310, COMP9007, COMP9120, STAT5002. | |||
Core Units |
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Without waiver, candidates for the Master of Data Science must complete: COMP5310, STAT5003, COMP5318, COMP5048. | |||
COMP5048 Visual Analytics |
6 | A It is assumed that students will have basic knowledge of data structures, algorithms and programming skills. |
Semester 2 |
COMP5310 Principles of Data Science |
6 | Semester 1 |
|
COMP5318 Knowledge Discovery and Data Mining |
6 | A INFO9120 OR COMP5138 |
Semester 1 |
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 structure can also help students to better understand the concepts of Algorithms will be 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. 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 |
STAT5003 Computational Statistical Methods |
6 | P STAT5002 Note: Department permission required for enrolment |
Semester 2 |
Project Units |
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Candidates for the Master of Data Science must complete 24 credit points from Core and Elective units of study before enrolling in any Project units. | |||
Candidates who do not achieve a credit average may have their eligibility for the capstone project subject to review by the Academic Director. | |||
The minimum requirement for the Master of Data Science is 12 credit points of capstone project units. These can be completed either as the two 6 credit point units, COMP5707 and COMP5708, over two semesters, or as the 12 credit point unit, COMP5703, in one semester. | |||
COMP5703 Information Technology Project |
12 | P A candidate for the MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. N : COMP5702 OR COMP5704 OR COMP5707 OR COMP5708 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
COMP5706 IT Industry Placement Project |
6 | N COMP5703, COMP5702, COMP5704 Note: Department permission required for enrolment |
Semester 1 Semester 2 |
COMP5707 Information Technology Capstone A |
6 | N COMP5702 OR COMP5704 OR COMP5703. Eligible students of the IT Capstone Project may choose either COMP5703 or COMP5707/COMP5708. Note: Department permission required for enrolment A candidate for the MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the IT Capstone project will be required to complete both COMP5707 (6 CPS) and COMP5708 (6 CPS), totaling 12 CPS. |
Semester 1 Semester 2 |
COMP5708 Information Technology Capstone B |
6 | C COMP5707 N COMP5702 OR COMP5704 OR COMP5703. Eligible students of the IT Capstone Project may choose either COMP5703 or COMP5707/COMP5708. Note: Department permission required for enrolment A candidate for the MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the IT Capstone project will be required to complete both COMP5707 (6 CPS) and COMP5708 (6 CPS), totaling 12 CPS. |
Semester 1 Semester 2 |
Data Science Elective Units |
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Candidates for the Master of Data Science may complete a maximum of 12 credit points of Data Science elective units of study from the table below: | |||
COMP5046 Statistical Natural Language Processing |
6 | A Knowledge of an OO programming language Note: Department permission required for enrolment |
Semester 1 |
COMP5338 Advanced Data Models |
6 | A This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/ INFO9120 (Database Management Systems) or INFO2120/2820 (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 INFO9103 Software Development in JAVA |
Semester 1 |
COMP5425 Multimedia Retrieval |
6 | A COMP9007 or COMP5211. Basic Programming skills and data structure knowledge. |
Semester 1 |
INFO5060 Data Analytics and Business Intelligence |
6 | A The unit is expected to be taken after introductory courses or related units such as COMP5206 Information Technologies and Systems |
Summer Early |
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 |
Semester 2 |
QBUS6840 Predictive Analytics |
6 | P QBUS5001 or ECMT5001 |
Semester 1 |
Non-Data Science Elective Units |
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Candidates must complete a maximum of 12 credit points from the listed Non-Data Science Elective units, or units of study from any discipline deemed appropriate as a non-Data Science elective by the Academic Director. | |||
EDPC5012 Evaluating Learning Tech. Innovation |
6 | Semester 1 |
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EDPC5025 Learning Technology Research Frontiers |
6 | Semester 2 |
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PHYS5033 Environmental Footprints and IO Analysis |
6 |
Minimum class size of 5 students. |
Semester 1 Semester 2 |
For more information on degree program requirements visit CUSP https://cusp.sydney.edu.au