Postgraduate Coursework
Graduate Diploma of Data Science
2020
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The Graduate Diploma in Data Science will further develop your technical and analytical skills needed to manage and gain useful insights from massive data sets.
This qualification complements the following multi-disciplinary areas:
- computer science
- information technology
- information systems
- statistics
- mathematics
- engineering or a program with substantial quantitative competencies.
Your coursework will also include a project-based assessment giving you the freedom to focus on what you are passionate about.
Career opportunities
Graduating from this course may lead to a career as a business analyst, data scientist, data analyst, data engineer and 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
For further information on credit transfer and how to apply visit ASK CDU
Course fees
Non-Commonwealth supported places
Full fee paying places are available in this course.
2019: Information about fees and charges can be found at Fees and Payments and 2019 HE Units and Fees.
2020: Information about fees and charges for 2020 will be published no later than 1 December 2019.
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.
International applications
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.
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This course is accredited by the University in accordance with the Higher Education Standards.
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Australian qualification framework
This course is recognised in the Australian Qualifications Framework at Level 8.
Contact details
For further information about the course, enrolment procedures, closing dates and other administrative issues please contact Student Central on:
Course structure
The course consists of core units, specialist elective units and elective units.
The core units of this program will allow you to explore a range of data science concepts.
Specialist electives will grow your knowledge in areas such as statistics and decision making.
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 Graduate Diploma of Data Science online or on campus. Access to the internet is required for distance learning, with access to high speed broadband highly recommended.
- 2020 HE unit offerings will be published on 1 October 2019.
- 2020 HE unit fees will be published no later than 1 December 2019.
- Always refer to the Course Catalogue for the latest information about your course prior to enrolment.
A candidate must successfully complete units totalling 80 credit points (cp) as detailed below. All units are valued at 10 credit points unless indicated.
| Unit type | Credit Points | Specific requirements |
|
Core Units (6 units) |
60cp |
Compulsory Core units totalling 60 credit points as detailed below.
BIS551 Business Intelligence and Data Mining |
|
Specialist Electives (2 units) |
20cp |
Specialist Elective units totalling 20 credit points as detailed below.
BUS501 Decision Making |
| 80cp | Total Credit Points |
Students who commenced in or after 2013
The grade of "PC" cannot be counted towards a Graduate Diploma AQF Level 8 course award.
Students should refer to the current Grading Policy and Common Course Rules for further information.
Study plan
- 2020 HE unit offerings will be published on 1 October 2019.
- 2020 HE unit fees will be published no later than 1 December 2019.
- Always refer to the Course Catalogue for the latest information about your course prior to enrolment.
Students commencing in Semester 1
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 |
| Semester 1 | Semester 2 | ||
| Year 1 | |||
| PRT551 Project Management, Risk and Reliability | CO | PRT563 Advanced Data Management (replaces PRT561) | CO |
| BIS551 Business Intelligence and Data Mining | CO | PRT456 Information Systems Management | CO |
| PRT564 Data Analytics and Visualisation (replaces PRT562) | CO | HIT400 Discrete Structures | CO |
| Specialist Elective | SE | Specialist Elective | SE |
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.
| Legend: | CO = Core Unit | SE = Specialist Elective |
| Semester 1 | Semester 2 | ||
| Year 1 | |||
| PRT563 Advanced Data Management (replaces PRT561) | CO | ||
| PRT456 Information Systems Management | CO | ||
| HIT400 Discrete Structures | CO | ||
| Specialist Elective | SE | ||
| Year 2 | |||
| PRT551 Project Management, Risk and Reliability | CO | ||
| BIS551 Business Intelligence and Data Mining | CO | ||
| PRT564 Data Analytics and Visualisation (replaces PRT562) | CO | ||
| Specialist Elective | SE | ||