Graduate Diploma of Data Science
2023 Deakin University Handbook
Year | 2023 course information |
---|---|
Award granted | Graduate Diploma of Data Science |
Course Map | This course map is for new students commencing from Trimester 1 2023. This course map is for new students commencing from Trimester 2 2023. This course map is for new students commencing from Trimester 3 2023. Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central. |
Campus | Offered at Burwood (Melbourne) |
Online | 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 online. |
Course sub-headings
- Course overview
- Indicative student workload
- Career opportunities
- Participation requirements
- Mandatory student checks
- Fees and charges
- Course Learning Outcomes
- Course rules
- Course structure
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. More information available at Disability support services.
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.
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 mandatory checks, travel and stationery.
Use the Fee estimator to see course and unit fees applicable to your course and type of place. Further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods is available on our Current students fees website.
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. |
Approved by Faculty Board 27 June 2019
Course rules
To complete the Graduate Diploma of Data Analytics, students must attain 8 credit points.
The course is structured in two parts:
- Part A: Fundamental Data Analytics Studies (4 credit points),
- Part B. Core Data Science Studies (4 credit points), plus
- Completion of STP050 Academic Integrity (0-credit point compulsory unit)
Depending upon prior qualifications and/or experience, you may receive credit for Part A.
Course structure
Core
Mandatory unit for all entry levels
STP050 | Academic Integrity (0 credit points) |
Part A: Fundamental Data Analytics Studies
SIT718 | Real World Analytics |
SIT731 | Data Wrangling |
SIT787 | Mathematics for Artificial Intelligence |
Plus one level 7 SIT or MIS coded unit#
Part B: Core Data Science Studies
SIT720 | Machine Learning |
SIT741 | Statistical Data Analysis |
SIT742 | Modern Data Science |
Plus one level 7 SIT or MIS coded unit#
# Excluding SIT771, SIT772, SIT773, SIT774
Other course information
Course duration - additional information
Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.
Further information
Student Central can help you with course planning, choosing the right units and explaining course rules and requirements.
- Contact Student Central