Master of Data Science (MC-DATASC)

You’re currently viewing the 2020 version of this course

Overview

Award title	Master of Data Science
Year & campus	2020 — 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;
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
Students entering the program with the equivalent of both Statistics and Computer Science majors accelerate through to a 150 point program.

Dependent upon educational background students may need to take up to 50 points of the following:

Student 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

Student 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	Summer Term (On Campus - Parkville) Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5

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) Winter Term (On Campus - Parkville)	12.5
COMP90051	Statistical Machine Learning	Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5

Capstone Project

Students are expected to have completed at least two semesters of their course before beginning the Data Science 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 sufficiently high weighted average mark 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 first have agreement from a supervisor before enrolling in the research 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

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
		Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville) Not available in 2020
GEOM90008	Foundations of Spatial Information	Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5
GEOM90018	Spatial Databases	Semester 1 (On Campus - Parkville)	12.5
GEOM90006	Spatial Analysis	Semester 2 (On Campus - Parkville)	12.5
GEOM90007	Information Visualisation	Semester 2 (On Campus - Parkville)	12.5
MAST90110	Analysis of High-Dimensional Data	Not available in 2020	12.5
MAST90111	Advanced Statistical Modelling	Semester 2 (On Campus - Parkville)	12.5
MAST90051	Mathematics of Risk	Semester 2 (On Campus - Parkville)	12.5
MAST90014	Optimisation for Industry	Semester 1 (On Campus - Parkville)	12.5
MAST90027	Practice of Statistics & Data Science	Semester 2 (On Campus - Parkville)	12.5
MAST90059	Stochastic Calculus with Applications	Not available in 2020	12.5
MAST90081	Advanced Probability	Semester 1 (On Campus - Parkville)	12.5
MAST90019	Random Processes	Semester 2 (On Campus - Parkville)	12.5
MAST90125	Bayesian Statistical Learning	Semester 2 (On Campus - Parkville)	12.5
MAST90082	Mathematical Statistics	Semester 1 (On Campus - Parkville)	12.5
COMP90054	AI Planning for Autonomy	Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5
COMP90057	Advanced Theoretical Computer Science	Semester 2 (On Campus - Parkville)	12.5
COMP90014	Algorithms for Bioinformatics	Semester 2 (On Campus - Parkville)	12.5
COMP90016	Computational Genomics	Semester 1 (On Campus - Parkville)	12.5
COMP90046	Constraint Programming	Not available in 2020	12.5
COMP90043	Cryptography and Security	Semester 2 (On Campus - Parkville)	12.5
COMP90048	Declarative Programming	Semester 1 (On Campus - Parkville)	12.5
COMP90020	Distributed Algorithms	Semester 1 (On Campus - Parkville)	12.5
COMP90015	Distributed Systems	Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5
COMP90007	Internet Technologies	Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5
COMP90018	Mobile Computing Systems Programming	Semester 2 (On Campus - Parkville)	12.5
COMP90025	Parallel and Multicore Computing	Semester 2 (On Campus - Parkville)	12.5
COMP90045	Programming Language Implementation	Semester 2 (On Campus - Parkville)	12.5
COMP90042	Natural Language Processing	Semester 1 (On Campus - Parkville)	12.5
COMP90056	Stream Computing and Applications	Semester 2 (On Campus - Parkville)	12.5
ISYS90035	Knowledge Management Systems	Semester 1 (On Campus - Parkville)	12.5
COMP90073	Security Analytics	Semester 2 (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
SCIE90012	Science Communication	Semester 2 (On Campus - Parkville)	12.5
SCIE90013	Communication for Research Scientists	Semester 1 (On Campus - Parkville) Winter Term (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5
EDUC90839	Science in Schools	Not available in 2020	12.5
SCIE90017	Science and Technology Internship	Summer Term (On Campus - Parkville) Semester 1 (On Campus - Parkville) Semester 2 (On Campus - Parkville)	12.5

Last updated: 18 December 2020