- Overview
- Learning outcomes
- Program delivery and structure
BP340 - Bachelor of Data Science
Plan: BP340 - Bachelor of Data Science
Campus: City Campus
Program delivery and structure
Approach to learning and assessmentWork integrated learningProgram structureProgram transition planApproach to learning and assessment
Your learning experiences will contain a broad mix of study modes, including lectures, tutorials, practical classes, studios, project work and seminars, using face-to-face, on-line, and other flexible delivery mechanisms.
Assessment is designed to give you opportunities to demonstrate your capabilities. You will find that the forms of assessment used may be different for each course, depending on the course objectives and learning outcomes.
Your assessment in this program will include all or some of the following:
- Examinations: an individual form of assessment where you are asked to demonstrate your ability to explain principles and to solve problems;
- Assignments and projects: some will require you to demonstrate an ability to work alone, while some will involve group work requiring you to be part of team with other students;
- Reflective journals: where you pause to consider what you have learnt and reflect on the further development of the related capability;
- Assessed tutorials or presentations: a form of in-class test which you will be required to complete either individually or as a team:
- Self-assessment and peer-assessment: for assessment activities such as seminars you may be asked to assess your own work, the work of your group, or the work of other groups. This is part of equipping you to become more independent in your own learning and to develop your assessment skills.
The assessments you complete, with the exception of exams, will enable the teaching staff to provide you with feedback on your progress. This will enable you to improve your performance in the future.
If you have special needs or a disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You should contact the Program Manager or Equitable Learning Services.
Top of pageWork integrated learning
RMIT is committed to providing students with an education that strongly links formal learning with workplace 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.
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.
COSC2816 Case Studies in Data Science 1 (12CP) and COSC2817 Case Studies in Data Science 2 (12CP) include a work integrated learning experience in which your knowledge and skills will be applied and assessed in a simulated workplace context where feedback from data scientists working in industry is integral to your experience.
ONPS2186 Science Project 1 (12CP) and ONPS2669 Science Project (24CP) are capstone courses designed to provide you with hands-on practical experience. All your learning activities in these courses are based on applying your data science knowledge in a process of planning and executing a substantial research-based project or industry-sponsored capstone project experience.
Top of pageProgram Structure
| To graduate you must complete the following: All courses listed may not be available each semester | ||||||||||||||||||||||||||||||||
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Year One of Program |
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| Complete the following Seven (7) Courses: | ||||||||||||||||||||||||||||||||
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| If you have not completed VCE Maths Methods or VCE Specialist Maths or equivalent, you must complete the following course: | ||||||||||||||||||||||||||||||||
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| Select and Complete One (1) Course from any: | ||||||||||||||||||||||||||||||||
| University Elective | ||||||||||||||||||||||||||||||||
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Year Two of Program |
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| Select and Complete One (1) of the following Courses: | ||||||||||||||||||||||||||||||||
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Year Three of Program |
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| Complete the following Five (5) Courses: | ||||||||||||||||||||||||||||||||
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| Select and Complete Two (2) Courses from any: | ||||||||||||||||||||||||||||||||
| University Elective | ||||||||||||||||||||||||||||||||
Program transition plan
Very Important: This plan is being phased out.
BP340 - Bachelor of Data Science plan has been discontinued and will no longer accept new students after Semester 2 2022. The plan will be taught out to current students until semester 2, 2026. If you are unable to complete your program by the end of 2026, you may consider applying to other programs within RMIT subject to entrance requirements. You may also consider applying to the new plan:
BP340P23 Bachelor of Data Science
For more information and advice on your enrolment, please contact your program manager, Yongli Ren (yongli.ren@rmit.edu.au).
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