Data Science Swinburne University of Technology

Overview

The Master of Data Science at Swinburne University of Technology is a comprehensive programme tailored for individuals seeking to deepen their expertise in data analytics. This course is structured to provide students with the knowledge and skills necessary to thrive in a data-centric environment, enabling them to derive meaningful insights from various data sources. By engaging with contemporary tools and leading-edge techniques, graduates will be well-prepared to contribute to data-driven decision-making processes across multiple industries.

Swinburne is consistently ranked among the top universities in Australia for Computer Science and Electronics. The university's strong industry connections and partnerships foster a practical learning environment, allowing students to engage in real-world projects. With a global ranking within the top 100 for Computer Science and Engineering, Swinburne offers state-of-the-art facilities and resources that enhance the learning experience.

The tuition fees for the Master of Data Science are as follows:

• Domestic students: AUD 33,280 per year
• International students: AUD 43,280 per year

Visit the Fees and Funding section for a breakdown in your local currency.

The curriculum for the Master of Data Science is designed to ensure a robust understanding of the field. Students will complete a total of 200 credit points, which include:

• Core Units (12 units, 175 credit points)
• Electives (2 units, 25 credit points)

Core units encompass key topics such as:

• Introduction to Data Science
• Data Management for the Big Data Age
• Machine Learning
• Data Visualisation
• User-Centred Design
• Technology Application Project

Graduates of the Master of Data Science will find themselves well-equipped for various roles within the industry. Potential career paths include positions as:

• Statistician
• Business Analyst
• Data Engineer
• Data Architect

Alumni have successfully secured roles in prominent companies, contributing to sectors that rely heavily on data analytics, thus showcasing the programme's effectiveness in preparing students for the workforce.