Master of Data Science

At the end of the program, students of the Master of Data Science can:

1. Reflect critically on a complex body of data science knowledge, research principles and methods to demonstrate mastery of professional practice.
2. Apply advanced cognitive and technical skills to analyse complex concepts in authentic data science scenarios.
3. Apply communication and collaboration skills in designing solutions to data sciences problems.
4. Use high level self-management skills to initiate, plan and execute a substantial data science focused project.

This course provides students with the opportunity to engage in a semester-long work-orientated project or a work-integrated experience. This provides an excellent springboard for employment following this degree. Career pathways available to data science graduates include data scientist, data analyst, machine learning scientist, statistician, informatician, computational scientist. Graduates of this course will also have the option to pursue further study at the PhD level.

Possible future job titles

Data Scientist, Data Analyst, Informatician, Computational Scientist

Higher education

Academic admission requirements (Band 6) may be satisfied through completion of one of the following:

• Bachelor degree; or
• Equivalent prior learning including at least five years relevant professional experience.

English Proficiency Requirements

Students can satisfy English competency requirements by showing any of the following:

• An IELTS Academic Overall band minimum score of 6.5 (no individual band less than 6.0).

• Completion of a Bachelor degree from a country specified by ECU.

• Completion of 0.375 EFTSL of study at postgraduate level or higher at an Australian higher education provider (or equivalent).

• Equivalent prior learning courses including at least five years relevant professional experience – as accepted by ECU.

• Completion of other tests, courses or programs as accepted by ECU.

Practicum placement

Work Experience requirement

In the final semester of the course, students will complete a project involving integrated learning with a company, agency, or university academic in their discipline area.

Attendance requirements

Students will be expected to participate in a minimum of 456 hours working with an organisation on a project and produce a report on activities.

Clearances and/or Risk Management Protocols Required

Each project will have an agreement for student placement. Each workplace will be inspected, and the appropriate forms completed indicating it is a safe work environment for students. Every student will be required to complete a risk assessment and management plan as part of this placement.

Reduce the time it takes to finish your degree. For eligible students, ECU offers credit and recognition of prior learning (CRPL). There are 2 CRPL options: 

• Credit transfer to acknowledge students’ formal learning at uni or TAFE. 

• Recognition of students’ informal learning gained outside of the education system (for example workplace experience or volunteer training.)

Data Science is an inter-disciplinary field, drawing on mathematics, statistics, and computer science, that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.

This course provides the necessary foundations in the disciplines of mathematics, statistics and computer science, and develops student knowledge and skills in some of the key tools and techniques relevant to data science. It also pays specific attention to ethical issues surrounding the manner in which data is gathered, stored and analysed/utilised.

Data Science is a significant area of growth and potential employment in Australia and the Asia-Pacific region.

Recommended Study Pattern

As a guide, core subjects in this degree can include: 

• Programming Principles
• Bio statistics
• Mathematical Fundamentals

As part of your application, you’ll be guided through how to get the right degree structure in place for you.