Unit of study table

Unit of study Credit points A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition Session

Master of Data Science

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

Master of Data Science

Candidates for the Master of Data Science must complete 24 credit points from the following core units:
COMP5310
Principles of Data Science
6    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
COMP5048
Visual Analytics
6    A It is assumed that students will have basic knowledge of data structures, algorithms and programming skills.
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 MDS students. Please apply for special permission for this unit of study.

Graduate Certificate in Data Science

Candidates for the Graduate Certificate in Data Science must complete all 24 credit points of the following core units:
COMP5310
Principles of Data Science
6    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 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 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

Project

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 Capstone Project
12    P A candidate for the MDS, 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 OR COMP5709
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 MDS, 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
COMP5709
IT Capstone Project - Individual
12    A A candidate for the MDS, MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study, and has a WAM of 75+ may take this unit
N COMP5702 OR COMP5703 OR COMP5704 OR COMP5707 OR COMP5708

Note: Department permission required for enrolment

Semester 1
Semester 2

Data Science Electives

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
Natural Language Processing
6    A Knowledge of an OO programming language
Semester 1
COMP5328
Advanced Machine Learning
6    A COMP5318
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 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 Main
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 1
Semester 2
QBUS6840
Predictive Analytics
6    P QBUS5001 or ECMT5001
Semester 1
Semester 2

Non-Data Science Electives

Candidates may 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.
CSYS5010
Introduction to Complex Systems
6      Semester 1
Semester 2
DATA5207
Data Analysis in the Social Sciences
6    A COMP5310

Note: Department permission required for enrolment

Intensive December
Semester 1
EDPC5012
Evaluating Learning Tech. Innovation
6      Semester 1
EDPC5025
Learning Technology Research Frontiers
6      Semester 2
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

For more information on degree program requirements visit CUSP https://cusp.sydney.edu.au