Master of Data Science
Course handbook
General Information
Overview
Advance your knowledge and understanding of data analytics with the Master of Data Science. This course is designed for postgraduate students who want to gain meaningful insights from data that comes from a variety of sources. You’ll develop skills in leading-edge techniques and learn the contemporary tools that relate to a data management lifecycle. After graduating you’ll be skilled to work at the forefront of data-driven decision-making and forecasting.
Study structure
To qualify for the Master of Data Science, students must complete 200 credit points.
Full-time study: 100 credit points/eight standard units of study per year
Part-time study: 50 credit points/four standard units of study per year
One credit point is equivalent to one hour of study per week per semester (including contact hours and private study)
See the course planner for an example degree structure.
Full-time study: 100 credit points/eight standard units of study per year
One credit point is equivalent to one hour of study per week per semester (including contact hours and private study)
See the course planner for an example degree structure.
| Units of study | Unit code |
|---|---|
| Core units | |
|
Creating Web Applications
Core unit, 12.5 credit points |
COS60004 |
|
Introduction to Data Science
Core unit, 12.5 credit points |
COS60008 |
|
Data Management for the Big Data Age
Core unit, 12.5 credit points |
COS60009 |
|
Technology Inquiry Project
Core unit, 12.5 credit points |
COS60010 |
|
Technology Design Project
Core unit, 12.5 credit points |
COS60011 |
|
User-Centred Design
Core unit, 12.5 credit points |
COS70004 |
|
Cloud Computing Architecture
Core unit, 12.5 credit points |
COS80001 |
|
Data Visualisation
Core unit, 12.5 credit points |
COS80025 |
| Units of study | Unit code |
|---|---|
| Core units | |
|
Technology Innovation Project
Core unit, 25.0 credit points |
COS70008 |
|
Big Data
Core unit, 12.5 credit points |
COS80023 |
| Units of study | Unit code |
|---|---|
| Core units | |
|
Machine Learning
Core unit, 12.5 credit points |
COS80027 |
|
Technology Application Project
Core unit, 25.0 credit points |
COS80029 |
| Elective units | |
|
Cyber-Ethics
Elective unit, 12.5 credit points |
CYB70001 |
|
Internship Project
Elective unit, 12.5 credit points |
ICT80004 |
|
Research Paper
Elective unit, 12.5 credit points |
ICT80007 |
|
Minor Thesis
Elective unit, 25.0 credit points |
ICT80014 |
Learning outcomes
At the completion of the Master of Data Science course, graduates will be able to:
- apply a coherent understanding of the concepts and practices within the field of Data Science as an effective member of diverse teams in a professional context
- critically analyse various Data Science scenarios, evaluate the existing knowledge base, and propose and justify effective and/or innovative solutions, including the choice of appropriate technology
- engage with solutions for practical applications with personal discipline, critical thinking, and scholarship skills
- communicate information proficiently to technical and non-technical audiences, including industry practitioners
- engage in regular professional reflection and development with a focus on continuous learning, time management and performance review
- apply knowledge of research principles and methods to solve diverse Data Science problems from scenarios relevant to science and/or industry and critically reflect on the appropriateness of the solution
Career opportunities
Graduates will have skills to take on roles such as data engineer, machine learning engineer, data analyst, data architect, junior data scientist, and customer support analyst. These roles commonly use skills in data science, data modelling, and data transformation with mathematics, programming, and statistics knowledge. Some industries use different names for these roles, such as business analyst, business analyst consultant, statistician, and data consultant. These roles also require the same skills but focus more on communicating with people.
Professional recognition
This course is accredited at the Professional Level by the Australian Computer Society (ACS).
Volume of learning
The Master of Data Science consists of 200 credit points. Units normally carry 12.5 credit points. A standard annual full-time load comprises 100 credit points and a part-time load comprises 50 credit points. The volume of learning of the Master of Data Science is typically 2 years.
Maximum Academic Credit
The maximum level of credit that can be granted for the Master of Data Science is 100 credit points (normally eight units).
Admission criteria
Information about Swinburne's general admission criteria can be found at Admissions at Swinburne - Higher Education webpage.
Interested in the Master of Data Science?
From state-of-the-art facilities to opportunities to engage with industry – this course is designed with your future in mind. Let's get started.