Course Information

Master of Data Science

Effective from 01-JAN-2025 : Code I97

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

Disclaimer

This course information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester. In particular please check the course requirements and the unit and unit set offerings, as these differ according to course delivery location.

Work Experience Option

Students have the opportunity to spend their final semester undertaking a professional placement embedded within a host organisation. Successful applicants for this capstone placement opportunity will enrol in and complete SCI6700 Professional Placement (Science and Mathematics).

Duty of care

Students, the WIL host organisation and the school's WIL Coordinator must complete a WIL documentation pack (which includes all required OSH and Risk Assessment documents) before the placement can commence. WIL host organisations may have additional clearance requirements of applicants, including evidence of police clearance, non-disclosure agreements or research ethics clearances. There may also be vaccination or other similar requirements, including those imposed by government or third-party placement hosts, that apply to Professional Placements which form part of your course. Please consider this requirement before applying for Professional Placement and speak with the WIL and Course Coordinator if this raises any concerns. You may not be able to complete the Professional Placement unit if you are unable to meet the placement requirements.

Attendance requirements

Students are required to complete a placement which is equivalent to one semester of full-time study. Whilst attendance is negotiated with the WIL host organisation, typically students will be expected to undertake a minimum of 300 hours over a maximum of 17 weeks. Typical full-time placements usually comprise 450 hours of professional placement.

Application process

Students are required to apply in writing to the Work Integrated Learning Coordinator (the student's Course Coordinator can advise who is the responsible staff member) upon successful completion of 120 credit points of study. Students should seek the advice of their Course Coordinator and the WIL Coordinator as to the appropriateness of pursuing the work placement option within their course structure. Successful applicants will need to complete any directed pre-placement preparation activities.

Enrolment process

Students will need permission from the WIL Coordinator to enrol in SCI6700 Professional Placement (Science and Mathematics).

Implications of failing

Where students do not successfully complete SCI6700 they will be required to enrol in SCI6108 the following semester.

Course Learning Outcomes

  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.

Admission requirements

Admission requirement (Band 6)

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

English Language requirement (Band 4)

English competency requirements may be satisfied through completion of one of the following:

  • IELTS Academic Overall band minimum score of 6.5 (no individual band less than 6.0);
  • Bachelor degree from a country specified in the Admissions Policy;
  • Successfully completed 0.375 EFTSL of study at postgraduate level or higher at an Australian higher education provider (or equivalent);
  • Where accepted, equivalent prior learning, including at least five years relevant professional experience; or
  • Other tests, courses or programs as defined in the Admissions Policy.

Australian Qualifications Framework (AQF) level

This course has been accredited by ECU as an AQF Level 9 Masters Degree (Coursework) Award.

Course Duration

  • Full Time: 2 Years
  • Part Time: 4 Years

Course Delivery

  • Joondalup: Full Time, Part Time
  • Online: Full Time, Part Time

Course Coordinator

Dr Kat O'MARA

Course Structure

Year 1 - Semester 1
Unit Code Unit Title Credit Points
CSI6208Programming Principles20
MAT5212Biostatistics20
MAT6105Mathematical Fundamentals20
Year 1 - Semester 2
Unit Code Unit TitleCredit Points
Students who commence the course midyear will complete their second and third semesters in reverse order.
MAT6104Applied Multivariate Statistics20
SCI6120Science Communication and Ethics20
CSI6209Artificial Intelligence20
Year 2 - Semester 1
Unit Code Unit Title Credit Points
MAT6206Machine Learning20
CSI6207Systems Analysis and Database Design20
MAT6100Time Series Forecasting20
Year 2 - Semester 2
Unit Code Unit Title Credit Points
SCI6108 ^Postgraduate Science Project60
OR the following unit for students with an approved professional placement
SCI6700 ^Professional Placement (Science and Mathematics)60

^ Core Option


Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

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