1. Overview

Course:	Master of Data Science (2022 and earlier) (2022)
CP Required:	48
Min FT Duration:	1.00 Years
Min PT Duration:	2.00 Years
Faculty/School:	Faculty of Engineering
Years Offered:	2022, 2021, 2020, 2019, 2018, 2017, 2016

2. Requirements

To qualify for the award of the Master in Data Science, candidates must complete 48 credit points, including:

i. 24 credit points of Core units of study: COMP5310, COMP5318, COMP5048, STAT5003.

ii. 12 credit points of Project units.

iii. a maximum of 12 credit points of Non Data Science Elective units of study, as approved by the Academic Director.

Please note that students with a Graduate Certificate in Data Science still need to complete 48 cpts in a subsequent Master of Data Science - there is no credit transfer. The Associate Dean will give a waiver for COMP5310 to holders of a Graduate Certificate of Data Science, and affected students enrol in a third Elective Unit instead.

In cases where the Associate Dean waives the requirement for a student to complete a compulsory unit of study (under 46(1) of the Coursework Policy 2014) the student will be required to select Core or Data Science Elective units which complement their prior background and qualifications (subject to assessment by the Academic Director) as may be necessary to satisfy the requirements of the degree.

- 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.

For further details of the course rules, see the Faculty Handbook at http://sydney.edu.au/handbooks

3. Semesters

Note: This order of units is a guide only. Where core units are available in both Semester 1 and Semester 2, they can be switched with an alternative unit in this guide.

Note: The tables above assume completion in 1 year by taking a full-time load of 24 credit points per semester. When studied part-time, the duration of the Master of Data Science extends correspondingly.

4. Pathways

No streams/majors defined for this course version.

5. Unit Blocks

Block 1 - Data Science Core

Unit Code	Unit Name	CP	Sessions Offered
COMP5048	Visual Analytics	6	Semester 1 Semester 2
COMP5310	Principles of Data Science	6	Semester 1 Semester 2
COMP5318	Machine Learning and Data Mining	6	Semester 1 Semester 2
STAT5003	Computational Statistical Methods	6	Semester 1 Semester 2

Note: Without waiver, candidates must complete: COMP5310, STAT5003, COMP5318, COMP5048.

Please note that students with a Graduate Certificate in Data Science still need to complete 48 cpts in a subsequent Master of Data Science - there is no credit transfer. In this case, we give however a waiver for COMP5310, and affected students enrol in a third Elective Unit instead. For a visualisation, see the following diagram: http://sydney.edu.au/engineering/it/~roehm/docs/GCDS-to-MDS-Pathway.png

Block 2 - Data Science Project (Min CP: 12,Max CP: 12)

Unit Code	Unit Name	CP	Sessions Offered
DATA5703	Data Science Capstone Project	12	Semester 1 Semester 2
DATA5707	Data Science Capstone A	6	Semester 1 Semester 2
DATA5708	Data Science Capstone B	6	Semester 1 Semester 2
DATA5709	Data Science Capstone Project – Individual	12	Semester 1 Semester 2

Note: A candidate for the Master of Data Science must complete 24 credit points from Core and Elective units of study before taking Data Science Capstone 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.

Block 3 - Data Science Elective (Max CP: 18)

Unit Code	Unit Name	CP	Sessions Offered
COMP5046	Natural Language Processing	6	Semester 1
COMP5328	Advanced Machine Learning	6	Semester 2
COMP5329	Deep Learning	6	Semester 1
COMP5338	Advanced Data Models	6	Semester 2
COMP5349	Cloud Computing	6	Semester 1
COMP5425	Multimedia Retrieval	6	Semester 1
INFO5060	Data Analytics and Business Intelligence	6	Int July
INFO5301	Information Security Management	6	Semester 1 Semester 2
QBUS6810	Statistical Learning and Data Mining	6	Semester 1 Semester 2
QBUS6840	Predictive Analytics	6	Semester 1

Note: The prerequisites for QBUS6810 and QBUS6840 are waived for Master of Data Science students. Please apply for an enrolment exception request (EER) for these units.

Block 4 - Non Data Science Electives (Max CP: 12)

Unit Code	Unit Name	CP	Sessions Offered
CSYS5010	Introduction to Complex Systems	6	Semester 1 Semester 2
DATA5207	Data Analysis in the Social Sciences	6	Semester 1 Int December
EDPC5012	Evaluating Learning Technology Innovation	6	Semester 1
EDPC5025	Learning Technology Research Frontiers	6	Semester 2
ITLS6107	Applied GIS and Spatial Data Analytics	6	Semester 2 Summer Main
PHYS5033	Environmental Footprints and IO Analysis	6	Semester 1 Semester 2

Note: Non Data Science electives may be chosen from any discipline, as appropriate and approved by Academic Director.