Master of Data Science (MC-DATASC)

Overview

Award title	Master of Data Science
Year & campus	2025 — Parkville
CRICOS code	092791B
Fees information	Subject EFTSL, level, discipline and census date
Study level & type	Graduate Coursework
AQF level	9
Credit points	200 credit points
Duration	24 months full-time or 48 months part-time

The management and analysis of big data are becoming increasingly important in commerce, industry and applied science.

Data Science is a rapidly growing field that has evolved to address this need and sits at the intersection of statistics and computer science. Data science practitioners are accomplished in both areas and in order to keep pace with this changing field they need a sound background in both statistics and computer science.

The Master of Data Science is a professional entry program that combines these disciplines in a single coordinated program. It aims to prepare students for a career in data analytics, equipping them with the technological abilities and analytical skills needed to manage and gain insights from large and complex collections of data.

The program emphasises the use of statistical tools, techniques and methods along with in-depth analysis and evaluation, to solve real-world problems in the data realm.

Links to further information

This course is available in My Course Planner

My Course Planner is an interactive web application that allows you to explore your study options and decide which subjects and major(s), minors and/or specialisations are right for you.

Entry requirements

1. In order to be considered for entry, applicants must have completed:

Any undergraduate degree, in any discipline, with a weighted average mark of at least 65%, or equivalent; and
A 12.5 point subject from computer science or related disciplines whose content is focused on computer programming (taken at any tertiary year level); and
MAST10006 (Calculus 2) or equivalent; and
MAST10007 (Linear Algebra) or equivalent

Meeting these requirements does not guarantee selection.

2. In ranking applications, the Selection Committee will consider:

Prior academic performance.

3. The Selection Committee may seek further information to clarify any aspect of an application in accordance with the Academic Board rules on the use of selection instruments.

4. Applicants are required to satisfy the University’s English language requirements for graduate courses. For those applicants seeking to meet these requirements by one of the standard tests approved by the Academic Board, performance band 6.5 is required.

Notes:

Students entering the program with the approved equivalent of both Statistics and Computer Science majors may accelerate through to a 150-point program.

In order to be eligible for guaranteed Commonwealth Supported Place (for domestic students) or international fee place in the Master of Data Science, students must:

Complete an Australian Year 12 or the International Baccalaureate (IB) in 2018 or later either:
- in Australia; or
- outside Australia and be an Australian citizen; and

Achieve an ATAR (or notional ATAR) of 95.00 or above;
Enrol immediately in an undergraduate degree at the University of Melbourne or be granted deferral in the year following Year 12;
Successfully complete an undergraduate degree in one of the above-mentioned majors at the University of Melbourne;
Commence the Master of Data Science within 18 months of completing the undergraduate degree.

Inherent requirements (core participation requirements)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to providing support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website. http://www.services.unimelb.edu.au/disability/

Intended learning outcomes

On completion of the course, students should be able to:

Demonstrate a detailed technical understanding of the key advanced tools and methods used in data science;
Demonstrate expertise in machine learning methods and strategies for advanced data mining, expertise in database systems, and expertise in computational statistics.
Integrate and apply this expertise to produce solutions for real-world problems using public and private data sources.
Communicate findings from analyses clearly and effectively, including to an audience with a diverse background in science and/or industry;
Demonstrate a sophisticated awareness of ethical implications relevant to the use of data, and particularly “big data”;
Demonstrate a fundamental understanding of the theoretical underpinnings of algorithms in computer science and machine learning;
Demonstrate skills in the evaluation and synthesis of information from a range of sources and the ability to apply these skills to understand the international peer-reviewed scientific literature and primary research in data science and disciplines relevant to data science; and
Adapt to the different domains of application and to a rapidly evolving field.

Generic skills

Graduates will:

Have the ability to demonstrate advanced independent critical enquiry, analysis and reflection
Have a strong sense of intellectual integrity and the ethics of scholarship
Have in-depth knowledge of their specialist area
Reach a high level of achievement in writing, research or project activities, problem-solving and communication
Be critical and creative thinkers, with an aptitude for continued self-directed learning
Be able to examine critically, synthesise and evaluate knowledge across a broad range of disciplines
Have a set of flexible and transferable skills for different types of employment; and
Be able to initiate and implement constructive change in their communities, including professions and workplaces

Graduate attributes

Graduates have a sound knowledge of modern statistical methodology and computing that will equip them for a career in data science and enable their careers to develop as data science evolves.

Graduates will:

Have the ability to demonstrate advanced independent critical enquiry, analysis and reflection;
Have a strong sense of intellectual integrity and the ethics of scholarship;
Have in-depth knowledge of modern statistical methodology and computing
Reach a high level of achievement in writing, research or project activities, problem-solving and communication;
Be critical and creative thinkers, with an aptitude for continued self-directed learning;
Be able to examine critically, synthesise and evaluate knowledge across a broad range of disciplines;
Have a set of flexible and transferable skills for different types of employment; and
Be able to initiate and implement constructive change in their communities, including professions and workplaces

Course structure

The Master of Data Science requires the successful completion of 200 credit points.

75 credit points of Compulsory subjects
Completion of one Specialisation (consisting of 100 credit points)
25 credit point project option of:
25 credit points of Capstone Project

OR

25 credit points of Research Pathway Project

Subject Options

Compulsory

Code	Name	Study period	Credit Points
		Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)
MAST90139	Statistical Modelling for Data Science	Semester 1 (On Campus - Parkville)	12.5
MAST90138	Multivariate Statistics for Data Science	Semester 2 (On Campus - Parkville)	12.5
MAST90083	Computational Statistics & Data Science	Semester 2 (On Campus - Parkville)	12.5
COMP90024	Cluster and Cloud Computing	Semester 1 (On Campus - Parkville)	12.5
COMP90050	Advanced Database Systems	Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5
COMP90051	Statistical Machine Learning	Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5

Project option

Students select one Core Project option from:

Capstone Project

Code	Name	Study period	Credit Points
MAST90106	Data Science Project Pt1	Semester 1 (On Campus - Parkville)	12.5
MAST90107	Data Science Project Pt2	Semester 2 (On Campus - Parkville)	12.5

Research Pathway Project

Code	Name	Study period	Credit Points
MAST90108	Data Science Research Project Pt1	Summer Term (On Campus - Parkville) Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5
MAST90109	Data Science Research Project Pt2	Summer Term (On Campus - Parkville) Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5

Students who maintain a WAM of 80 in Data Science subjects will be eligible to undertake a 25-point individual research project in Data Science as the capstone project, to replace MAST90106 and MAST90107. Students must propose a research topic and confirm the name of project supervisor (from either the School of Mathematics and Statistics or the School of Computing and Information Systems) before seeking approval to enroll in MAST90108 and MAST90109 from the course coordinator.

Majors, minors & specialisations

Specialisation

Name	Credit Points
Foundational Data Science	100
Computational Data Science	100
Computational and Statistical Data Science	100
Statistical Data Science	100

Last updated: 18 April 2025