Master of Data Science

For more information on degree program requirements visit CUSP.

Unit outlines will be available through Find a unit outline two weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.
 

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

Data Science

Master of Data Science

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

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

Core

COMP5048
Visual Analytics
6    A Experience with data structures and algorithms as covered in COMP9103 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

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

Data Science Electives

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 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

This unit of study is not available in 2021

6    A Basic knowledge of information systems as 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 ECMT5001 or QBUS5001 or BUSS6002


Refer to the unit of study outline https://www.sydney.edu.au/units
Semester 1
Semester 2
QBUS6840
Predictive Analytics
6    P (QBUS5001 or ECMT5001) and BUSS6002


Refer to the unit of study outline https://www.sydney.edu.au/units
Semester 1
Semester 2
The prerequisites for QBUS6810 and QBUS6840 are waived for Master of Data Science students, who should request an enrolment exception when completing unit of study selection.

Non-Data Science Electives

Complete a maximum of 12 credit points from the following:.
CSYS5010
Introduction to Complex Systems
6      Semester 1
Semester 2
DATA5207
Data Analysis in the Social Sciences
6   
Note: Department permission required for enrolment in the following sessions:Intensive December, Intensive February

Intensive December
Intensive February
Semester 1
EDPC5012
Evaluating Learning Tech. Innovation
6      Semester 1
EDPC5025
Learning Technology Research Frontiers
6      Semester 2
HTIN5005
Computational Approaches for Healthcare Data
6      Semester 1
ITLS6107
Applied GIS and Spatial Data Analytics

This unit of study is not available in 2021

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

Core

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

COMP9001
Introduction to Programming
6    N INFO1110 OR INFO1910
Semester 1
Semester 2
COMP9007
Algorithms

This unit of study is not available in 2021

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