- Overview
- Learning outcomes
- Program delivery and structure
GD202 - Graduate Diploma in Data Science
Plan: GD202 - Graduate Diploma in Data Science
Campus: City Campus
Program delivery and structure
Approach to learning and assessmentWork integrated learningProgram structureApproach to learning and assessment
The teaching approach in this program is designed to foster your development as an independent learner so you will be able to extend your capabilities once you graduate. The teaching method includes lectorial,tutorial, workshop, practical sessions, studios, project work and seminars, using face-to-face, intensive,and online provision of materials. You will be expected to complete all prescribed out-of-class learning activities in preparation of scheduled face-to-face and online classes, and encouraged to extend your learning through additional recommended readings and online activities. Of particular importance is the time spent inpractice, laboratory based and work integrated learning activities that will develop your employability skills and capabilities.
Several courses in the program are delivered online, rather than on-campus, and you are likely to find that other courses transition to online delivery as you progress through the program. All courses use Canvas for electronic provision of course material, tutorial problems and/or other relevant documents.
Assessment is designed to give you the opportunity to demonstrate your capabilities. Various forms of assessment will be used throughout the program since the assessment you undertake will be appropriate to the objectives and student learning outcomes for each course. Assessment may include class tests, quizzes, essays/reports, oral class presentations, group projects, research projects, laboratory projects, practical assignments, timed assessment, and reflective journals.
If you are living with a disability, long-term illness and/or a mental health condition, we can support you by making adjustments to activities in your program so that you can fully participate in your studies. To receive learning adjustments, you need to register with Equitable Learning Service.
The University considers the wellbeing and safety of all students, staff and the community to be a priority in on-campus learning and professional experience settings.
Credit Transfer and Recognition of Prior Learning
If you have already developed areas of skill and knowledge included in this program (for example, through prior studies or work experience), you can apply for credit once you have enrolled in this program. There is information on the RMIT University website about how to apply for Credit: https://www.rmit.edu.au/students/student-essentials/enrolment/apply-for-credit.
Top of pageWork integrated learning
RMIT is committed to providing students with an education that strongly links formal learning withworkplace experience. As a student enrolled in an RMIT program you will:
- undertake and be assessed on a structured activity that allows you to learn, apply and demonstrate your professional or vocational practice
- interact with industry and community when undertaking this activity
- complete an activity in a work context or situation that may include teamwork with other students from different disciplines.
- underpin your learning with feedback from interactions and contexts distinctive to workplace experiences.
Any or all of these aspects of a WIL experience may be in a simulated workplace learning environment.
In this program, you will be doing specific courses that focus on work integrated learning (WIL). You will be assessed on professional work in a work place setting (real or simulated) and receive feedback from those involved in your industry.
The work integrated learning (WIL) designated courses for this program are:
- COSC2669 Case Studies in Data Science
In this WIL course, you will interact with organisations (industry, government and community) through discipline relevant projects and activities. These interactions and the work context provide a distinctive sourceof feedback to you to assist your learning. The course COSC2669 Case Studies in Data Science includes a work integrated learning experiencein which your knowledge and skills will be applied and assessed in a simulated workplace context wherefeedback from data scientists working in industry is integral to your experience.
Please note: students may be required to undertake additional screening/compliance checks as advised by Government, Industry or RMIT University as the need arises. If applicable, further information will be providedonce enrolment has been completed.
International Students will need to check their Visa requirements and any work regulations/limitations before they can commence any WIL Activity. Further information can be found under the Visa Requirements for International Students section.
Top of pageProgram Structure
| To graduate you must complete the following: All courses listed may not be available each semester | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Select and Complete Seven (7) of the following courses: | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Complete the following One (1) course: | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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