Master of Data Science From Swinburne University of Technology

Program Overview

The Master of Data Science is designed to prepare students to work on the forefront of data-driven decision-making and forecasting.

Build on your existing undergraduate qualification and/or industry experience as you develop an in-depth understanding of activities and processes related to managing, interpreting, understanding and deriving knowledge from large data sets.

In this course, you’ll learn how to gain meaningful insight from data obtained from business, government, scientific and other sources. Expand your knowledge and understanding of computer science and data analytics, develop skills in state-of-the-art techniques and contemporary tools covering the entire data management lifecycle.



  Course Duration

24 Months

  Tuition Fee

AU$ 37,080


IELTS: 6.5 TOEFL: 79


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Units of study

Core units

Complete the following 12 units (175 credit points):

  • COS60004 Creating Web Applications 
  • COS60008 Introduction to Data Science 
  • COS60009 Data Management for the Big Data Age
  • COS60010 Technology Enquiry Project
  • COS60011 Technology Design Project
  • COS70004 User-Centred Design
  • COS70008 Technology Innovation Project* (25 credit points) 
  • COS80001 Cloud Computing Architecture
  • COS80023 Big Data *
  • COS80025 Data Visualisation
  • COS80027 Machine Learning *
  • COS80029 Technology Application Project  (25 credit points) *

The admission requirements for Master of Data Science consist of:

  • a completed bachelor degree (or higher award) in any discipline from a recognised higher education institution or equivalent
  • successful completion of the Postgraduate Qualifying Program at Swinburne

English language requirements

  • IELTS overall band of 6.5 (Academic Module) with no individual band below 6.0

Course fees
A$ 37080

Graduate will have skills in state-of-the-art techniques and experience in contemporary tools covering a variety of aspects of the entire data management lifecycle, allowing them to work on the forefront of data-driven decision making and forecasting.


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