Part A: Course Overview
Course Title: Data Science Postgraduate Project
Credit Points: 24.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2667 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 2 2017, Sem 1 2018, Sem 2 2018, Sem 1 2019, Sem 2 2019, Sem 1 2020, Sem 2 2020, Sem 1 2021, Sem 2 2021 |
COSC2667 |
City Campus |
Postgraduate |
175H Computing Technologies |
Face-to-Face |
Sem 1 2022, Sem 2 2022, Sem 1 2023, Sem 2 2023, Sem 1 2024, Sem 2 2024, Sem 1 2025 |
Flexible Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2667 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
PGRDFlex18 (ZZZZ) |
COSC2667 |
City Campus |
Postgraduate |
175H Computing Technologies |
Face-to-Face |
PGRDFlex23 (All) |
Course Coordinator: Dr. Ke Deng
Course Coordinator Phone: +61 3 9925 3202
Course Coordinator Email: ke.deng@rmit.edu.au
Course Coordinator Location: 14.9.12
Course Coordinator Availability: by appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
Enforced Pre-Requisite Courses
Successful completion of the following course/s:
Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.
For your information go to RMIT Course Requisites webpage.
If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.
Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.
Please follow the link for further information on how to apply for credit for prior study or experience.
Course Description
This capstone course is designed to provide you with hands-on practical experience analysing data in a project environment.
The emphasis is on understanding and working within a professional environment and integrating all the skills and knowledge that you have acquired from your previous courses into a solid base to progress from into your professional life.
This course includes a Work Integrated Learning experience in which your knowledge and skills will be applied and assessed in a real or simulated workplace context and where feedback from industry and/ or community is integral to your experience.
Objectives/Learning Outcomes/Capability Development
Program Learning Outcomes
This course contributes to the program learning outcomes for the following program(s):
MC267 - Master of Data Science
PLO 1 Enabling Knowledge
PLO 3 Problem Solving
PLO 4 Communication
PLO 7 Research and Scholarship
For more information on the program learning outcomes for your program, please see the program guide.
On completion of this course you should be able to:
- Use research principles and apply appropriate methods to analyse, theorise and justify conclusions about new situations in data science professional practice and/or research;
- Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship;
- Apply mastery of theoretical knowledge and reflect critically on theory and professional practice;
- Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and presentation of your project deliverables.
Overview of Learning Activities
This is a project-based course where you learn through meetings and informal discussions with other students, the project manager and client. Your learning is in the ’doing’, where you will carry out all the necessary steps to successfully complete your project.
All your learning activities in this course 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.
There are no lectures in this course, but weekly or fortnightly meetings with the supervisor(s), other students working on the related projects and where applicable industry partners or other collaborators.
Each project is different and has its own individual goals and deliverables.
Overview of Learning Resources
You will use computer laboratories and relevant software provided by the University. You will be able to access course information and learning materials through myRMIT (Canvas).
Overview of Assessment
This course has no hurdle requirements.
Assessment Task 1: Project Plan
Weighting: 15%
This assessment task supports CLOs 1, 2
Assessment Task 2: Final oral/video presentation
Weighting: 10%
This assessment supports CLO 4
Assessment Task 3: Final written report
Weighting: 40%
This assessment supports CLOs 1, 2, 3, 4
Assessment Task 4: Presentation and team performance
Weighting: 35%
This assessment supports CLOs 1, 2, 3, 4