* Ranking Method
The rankings of data science programs derive from a combination of two main sources: The Graduate Outcomes Survey (GOS) and the Student Experience Survey (SES).
- The GOS targets recent graduates from Australian higher education institutions. It provides insights into employment outcomes and the relevance of skills in the workforce. Annually, it gathers responses from over 130,000 graduates across various study levels.
- The SES assesses the current higher education student experience in Australia. It evaluates measurable aspects linked to learning outcomes that institutions can influence. It surveys over 175,000 undergraduates and 65,000 postgraduates annually.
For data science programs, we focused on responses within the broader field of Computing and Information Systems (postgraduate) from both surveys. The final list was limited to universities offering an online master's program in data science.
UNSW ranked first due to its outstanding performance in employment outcomes and median salary. JCU was ranked second, partly because of its highly rated support services.
RMIT's inclusion in the top three pertains to its focus on strategy, leadership, and innovation in the age of AI. With data science evolving alongside AI, courses with reduced techhnical content may become increasingly relevant.
Graduate Certificate in Data Science
A useful pathway to a data science master's is an online Graduate Certificate in Data Science. The course consists of four subjects from a master's program and normally has lower entry requirements. It is a way to both test out and qualify for a master's program.
This certificate develops essential data science skills. Topics cover artificial intelligence, consumer analytics, machine learning, etc. It's designed for those looking to pivot their career into the field or enhance their current role with data-driven decision-making skills.
Completing a grad cert can significantly boost your career. It offers a postgraduate qualification in a growing field, with options to progress to a graduate diploma or master's. This course suits anyone aiming to advance in the tech-driven job market without pausing their career.
Related: Graduate Certificate in Data Science Online
Is Masters Enough to Be a Data Scientist?
For many people, a data science master's degree is sufficient to become a data scientist. This is especially true if you follow our roadmap for how to become a data scientist, which includes gaining relevant job experience as an intermediate step.
Before undertaking your master's, you could, for example, gain experience as a data analyst, analytics manager, data engineer, or AI engineer.
Without experience, you might struggle to find a job opening even with a master's degree. Employers are looking for candidates with relevant experience who can hit the ground running.
To address the problem of inexperience, building a data science portfolio is an effective solution.
- Complete projects such as data visualisation projects, machine learning models, or analysis of real-world data sets.
- Choose activities that align with job requirements for the positions you're targeting.
Showcasing these projects in your portfolio can significantly enhance your job prospects. They are tangible evidence of your capabilities.