The Master of Data Science will further develop your technical and analytical skills needed to manage and interpret useful insights from massive data sets.
This qualification complements the following multi-disciplinary areas:
- computer science
- information technology
- information systems
- engineering or a program with substantial quantitative competencies.
Your coursework will also include a project-based assessment allowing you to focus on what you are passionate about.
Non-Commonwealth supported places
International tuition fees
The annual tuition fee for full time study of 80 credit points (1.0 EFTSL) for commencing students in 2020 is AUD $29,616.00.
These fees are subject to annual increases each year of your study, effective at the start of each calendar year.
CDU offers a number of scholarships to international students to assist with the cost of study.
Graduating from Master of Data Science may lead to career opportunities as a business analyst, data scientist, data analyst, data engineer or data manager.
Credit transfers & pathways
Pathways for Higher Education to Higher Education
For information about credit transfer available to students with complete or incomplete study at this or other Institutions refer to Pathways for Higher Education to Higher Education
English Language Requirements for International Students
For detail on English Language Entry Requirements please read the entry requirements provided for International students.
How to Apply
International applicants apply directly to CDU and should refer to the information provided for International future students Apply.
This course is accredited by the University in accordance with the Higher Education Standards.
Australian qualification framework
This course is recognised in the Australian Qualifications Framework at Level 9.
For further information about the course, enrolment procedures, closing dates and other administrative issues please contact Student Central on:
The course consists of core units, thesis research units, specialist elective units and general elective units.The core units of this program will allow you to explore a range of data science concepts.
Research units will allow you to complete an individual research thesis in a chosen area under the guidance of an academic supervisor. The thesis can be carried out within a university, industry, or government setting.
Specialist electives will grow your knowledge in areas such as advanced research methods, decision making and biostatistics.
General elective units will allow you to study complimentary courses or explore other areas of interests.
The course stresses both the importance of theory and the application of theory in practice through projects and practical exercises.
You can study a Master of Data Science online or on campus. Access to the internet is required for distance learning, with access to high speed broadband highly recommended.
A candidate must successfully complete units totalling 160 credit points (cp) as detailed below. All units are valued at 10 credit points unless indicated.
|Unit type||Credit Points||Specific requirements|
|80cp||Compulsory Core units totalling 80 credit points as detailed below.
|20cp||Specialist Elective units totalling 20 credit points selected from the following units as detailed below.|
Units totalling 40 credit points selected from the list of units below.
PRT820 Master Thesis (20cp) (repeatable) x 2
|20cp||Units totalling 20 credit points selected from postgraduate units offered by the University.|
|160cp||Total Credit Points|
Students who commenced in or after 2013
The grade of "PC" cannot be counted towards a Masters Degree by Coursework AQF Level 9 course award.
Students should refer to the current Grading Policy and Common Course Rules for further information.
The Recommended Study Plan provided below is suitable for a student commencing in semester 1 and enrolling in a standard load. Students entering this course with advanced standing, or wishing to reduce or vary their study plan due to work, personal, financial or other reasons should use the table as a guide to create an individual study plan.
|Legend:||CO = Core Unit||SE = Specialist Elective|
|E = Elective||R = Research|
|Semester 1||Semester 2|
|PRT531 Business Intelligence and Data Mining (replaces BIS551 from 2020)||CO||ENG417 Sustainability||CO|
|PRT551 Project Management, Risk and Reliability||CO||PRT563 Advanced Data Management (replaces PRT561)||CO|
|Specialist Elective||SE||PRT580 Discrete Structures (replaces HIT400 from 2020)||CO|
|Elective||E||PRT541 Information Systems Management (replaces PRT456 from 2020)||CO|
|PRT820 Master Thesis (20cp)||R||PRT820 Master Thesis (20cp)||R|
|PRT564 Data Analytics and Visualisation (replaces PRT562)||CO||PRT503 Entrepreneurship for Professionals||CO|
|Specialist Elective or Elective||SE/E||Specialist Elective or Elective||SE/E|
Students commencing in Semester 2
The Recommended Study Plan provided below is suitable for a student commencing in semester 2 and enrolling in a standard load. Students entering this course with advanced standing, or wishing to reduce or vary their study plan due to work, personal, financial or other reasons should use the table as a guide to create an individual study plan.
|Semester 1||Semester 2|
|PRT563 Advanced Data Management (replaces PRT561)||CO|
|PRT541 Information Systems Management (replaces PRT456 from 2020)||CO|
|PRT580 Discrete Structures (replaces HIT400 from 2020)||CO|
|PRT551 Project Management, Risk and Reliability||CO||PRT503 Entrepreneurship for Professionals||CO|
|PRT531 Business Intelligence and Data Mining (replaces BIS551 from 2020)||CO||PRT820 Master Thesis (20cp)||R|
|Specialist Elective||SE||Specialist Elective or Elective||SE/E|
|PRT564 Data Analytics and Visualisation (replaces PRT562)||CO|
|PRT820 Master Thesis (20cp)||R|
|Specialist Elective or Elective||SE/E|