Graduate Diploma of Data Science

2022 Deakin University Handbook

Note: You are seeing the 2022 view of this course information. These details may no longer be current. [Go to the current version]

Year	2022 course information
Award granted	Graduate Diploma of Data Science
Course Map	This course map is for new students commencing from Trimester 2 2022.
Campus	Offered at Burwood (Melbourne)
Cloud Campus	Yes
Duration	2 years part-time
Deakin course code	S677
Approval status	This course is approved by the University under the Higher Education Standards Framework.
Australian Qualifications Framework (AQF) recognition	The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 8.
INTERNATIONAL STUDENTS – Please note that due to Australian Government regulations, student visas to enter Australia cannot be issued to students who enrol in Deakin’s Cloud Campus.

Course sub-headings

Course overview

The Graduate Diploma of Data Science covers modern data science concepts, statistical data analysis, descriptive analytics and machine learning to equip you with the theory, methodologies, techniques and tools of modern data science. Through this course, you will develop the ability to confidently work with any type of data, to identify trends, make predictions, draw conclusions, drive innovations, make decisions and share information that influences people.

The sheer volume and complexity of data already available to businesses gives rise to challenges that must be solved by tomorrow’s graduates. Employers are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions. This course gives you essential skills in data analytics, enabling you to discover insights and support decision-making across a range of industries.

Indicative student workload

You can expect to participate in a range of teaching activities each week. This could include classes, seminars, practicals and online interaction. You can refer to the individual unit details in the course structure for more information. You will also need to study and complete assessment tasks in your own time.

Career opportunities

Graduates of this course are prepared for professional employment across all sectors as data science specialists. Professionals with a solid knowledge in data science and strong skills for analysing and interpreting data in today's data-rich economy are in high demand and may find careers as data analysts, data scientists, analytics programmers, analytics managers, analytics consultants, business analysts, management advisors, management analysts, business advisors and strategists, marketing managers, market research analysts and marketing specialists.

Participation requirements

Elective units may be selected that include compulsory placements, work-based training, community-based learning or collaborative research training arrangements.

Reasonable adjustments to participation and other course requirements will be made for students with a disability. Click here for more information.

Mandatory student checks

Any unit which contains work integrated learning, a community placement or interaction with the community may require a police check, Working with Children Check or other check.

Fees and charges

Fees and charges vary depending on your course, the type of fee place you hold, your commencement year, the units you choose and your study load. To find out about the fees and charges that apply to you, visit the Current students fees website or our handy Fee estimator to help estimate your tuition fees.

Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as photocopying or travel.

Course Learning Outcomes

Deakin Graduate Learning Outcomes	Course Learning Outcomes
Discipline-specific knowledge and capabilities	Develop specialised knowledge of data analytics concepts and technologies to solutions based on specifications and user requirements.
Communication	Communicate data analytical solutions as appropriate to the context to inform, motivate and effect change utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences.
Digital literacy	Use digital media to locate, collect and evaluate information from technical channels and apply information to design approaches and solutions that meet user requirements.
Critical thinking	Use the frameworks of logical and analytical thinking to evaluate data analytics information, technical problems and user requirements, and develop approaches to identify solutions.
Problem solving	Design solutions for automating data analysis processes by investigating technical and business problems; design and propose alternative solutions that improve services and user experiences.
Self-management	Demonstrate the ability to work in a professional manner, learn autonomously and responsibly in order to identify and meet development needs.
Global citizenship	Engage in professional and ethical behaviour in the design of data analytics systems, in a global context, in collaboration with diverse communities and cultures.