Handbook home
Master of Data Science (MC-DATASC)
Masters (Coursework)Year: 2024 Delivered: On Campus (Parkville)
About this course
Coordinator
Karim Seghouane
Coordinator
Michael Kirley
Contact
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Contact Stop 1
Future students:
- Further information: http://science.unimelb.edu.au/
Overview
Award title | Master of Data Science |
---|---|
Year & campus | 2024 — 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.
Entry requirements
1. In order to be considered for entry, applicants must have completed:
- Any undergraduate degree with a major in Computer Science, Data Science or Statistics with a weighted average mark of at least H3 (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 postgraduate 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 96.00 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 providingsupport 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.
- Demonstrate a sophisticated awareness of ethical implications relevant to the use of data, and particularly “big data”;
- 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
- Have the ability to adapt 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
This 200 point Master is based around
- 75 points of compulsory subjects: three core subjects in statistics (37.5 points), three core subjects in computer science (37.5 points);
- 25 points of a capstone project;
- 50 points of prerequisite subjects; and 50 points of electives.
Subject options
‘Prerequisite’ Subjects (up to 50 points depending on educational background)
It is expected that students admitted into the course will have either computer science or statistics background, though some students may have a mix of both.
- If a student has a computer science background, they will be required to complete statistics subjects in their first year as part of their elective component to satisfy the prerequisites for the Statistics core subjects
- If the student has Statistics background, they will need to complete computer science subjects in their first year as part of their elective component to satisfy the prerequisites for the Computer Science core subjects
Dependent upon educational background students may need to take up to 50 points of the following:
Students entering with a Computer Science background may need to take
Code | Name | Study period | Credit Points |
---|---|---|---|
MAST90105 | Methods of Mathematical Statistics | Semester 1 (On Campus - Parkville) |
25 |
MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
Students entering with a Statistics background may need to take:
Code | Name | Study period | Credit Points |
---|---|---|---|
COMP90041 | Programming and Software Development |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90038 | Algorithms and Complexity |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP20008 | Elements of Data Processing |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
INFO90002 | Database Systems & Information Modelling |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
Students entering with a Data Science background equivalent to both a Statistics and Computer Science major are able to accelerate through to a 150 point program.
Core (100 points):
Students must take:
Statistics core subjects
Code | Name | Study period | Credit Points |
---|---|---|---|
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 |
Computer Science core subjects
Code | Name | Study period | Credit Points |
---|---|---|---|
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 |
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 |
DATA SCIENCE RESEARCH PROJECT*
*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.
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 |
Electives: (50 points)
The remainder of the 200 points are made up subjects that may include Discipline Elective Subjects, Professional Skills Subjects, or elective subjects outside of the discipline electives with the approval of the course coordinator.
Discipline Elective Subjects
Code | Name | Study period | Credit Points |
---|---|---|---|
COMP90007 | Internet Technologies |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90014 | Algorithms for Bioinformatics | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90015 | Distributed Systems |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90016 | Computational Genomics | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90018 | Mobile Computing Systems Programming | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90020 | Distributed Algorithms | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90025 | Parallel and Multicore Computing | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90042 | Natural Language Processing | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90043 | Cryptography and Security | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90045 | Programming Language Implementation | Not available in 2024 | 12.5 |
COMP90046 | Constraint Programming | Not available in 2024 | 12.5 |
COMP90048 | Declarative Programming | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90054 | AI Planning for Autonomy |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90056 | Stream Computing and Applications | Not available in 2024 | 12.5 |
COMP90057 | Advanced Theoretical Computer Science | Not available in 2024 | 12.5 |
COMP90073 | Security Analytics | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90084 | Quantum Software Fundamentals | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90086 | Computer Vision | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90087 | The Ethics of Artificial Intelligence | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90088 | Cryptocurrencies & decentralised ledgers | Not available in 2024 | 12.5 |
COMP90089 | Machine Learning Applications for Health | Semester 2 (On Campus - Parkville) |
12.5 |
ISYS90035 | Knowledge Management Systems | Semester 2 (On Campus - Parkville) |
12.5 |
GEOM90006 | Spatial Data Analytics | Semester 1 (On Campus - Parkville) |
12.5 |
GEOM90007 | Information Visualisation | Semester 2 (Online) |
12.5 |
GEOM90008 | Spatial Data Management |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
MAST90014 | Optimisation for Industry | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90019 | Random Processes | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90027 | Practice of Statistics & Data Science | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90051 | Mathematics of Risk | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90059 | Stochastic Calculus with Applications | Not available in 2024 | 12.5 |
MAST90081 | Advanced Probability | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90082 | Mathematical Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90111 | Advanced Statistical Modelling | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90125 | Bayesian Statistical Learning | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90144 | Stochastic Optimisation | Semester 1 (On Campus - Parkville) |
12.5 |
Professional Skills Subjects
Students may take no more than 25 points of the following:
Code | Name | Study period | Credit Points |
---|---|---|---|
EDUC90839 | Science in Schools | Not available in 2024 | 12.5 |
SCIE90017 | Science and Technology Internship |
Summer Term (Off Campus)
Semester 1 (Off Campus)
Semester 2 (Off Campus)
|
12.5 |
SCIE90034 | Communicating Science at Work | Semester 1 (On Campus - Parkville) |
12.5 |
Last updated: 14 March 2024