Course Title: Data Science Project
Part A: Course Overview
Course Title: Data Science Project
Credit Points: 12.00
Important Information:
Please note that this course may have compulsory in-person attendance requirements for some teaching activities.
To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption.
Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT: https://policies.rmit.edu.au/document/view.php?id=209.
Please read the Student website for additional requirements of in-person attendance: https://www.rmit.edu.au/covid/coming-to-campus
Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance.
Course Coordinator: Yongli Ren
Course Coordinator Phone: +61 3 9925 2859
Course Coordinator Email: yongli.ren@rmit.edu.au
Course Coordinator Location: 14.09.007
Course Coordinator Availability: By Appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
Required prior study:
COSC2816 - Case Studies in Data Science
Course Description
This capstone course is designed to provide you with hands-on practical experience how to conduct data science projects, e.g. how to pre-process the data, how to transform the data, how to visualise the data, how to model the data, and how to evaluate the models.
The emphasis is on understanding and working within a professional environment and integrating all the related skills and knowledge that you have acquired from your previous courses into a solid base to progress from into your future professional life.
This course includes a Work Integrated Learning (WIL) 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.
Your WIL activity will be allocated to you at the start of the course.
An agreement including schedule and relevant insurance documentation is required to be completed before commencing each placement/internship (local and international). International placements/internships must be registered and processed through RMIT Global Mobility. In the case where a placement ends early, please refer to “Changes or cancellation of WIL activities” in the Work integrated learning (WIL) guideline.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes in the following programs:
BP340 Bachelor of Data Science
BP348 Bachelor of Data Science (Professional)
PLO1: Knowledge - Apply a broad and coherent set of knowledge and skills for developing user-centric computing solutions for contemporary societal challenges.
PLO2: Problem Solving - Apply systematic problem solving and decision-making methodologies to identify, design and implement computing solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.
PLO3: Cognitive and Technical Skill - Critically analyse and evaluate user requirements and design systems employing software development tools, techniques, and emerging technologies.
PLO4: Communication - Communicate effectively with diverse audiences, employing a range of communication methods in interactions to both computing and non-computing personnel.
PLO5: Collaboration and Teamwork - Demonstrate effective teamwork and collaboration by using tools and practices to manage and meet project deliverables.
Upon successful completion of this course you should be able to:
- CLO 1: Analyse and apply data science knowledge, principles and skills to develop data science solutions
- CLO 2: Apply problem solving and decision-making methodologies to identify, design and implement data-driven solutions.
- CLO 3: Critically analyse and evaluate user requirements and design data science solutions using software development tools, techniques and emerging technologies.
- CLO4: Communicate project deliverables to a variety of audiences through a range of modes and media. specifically, through written technical reports and presentation of your project deliverables
- CLO 5: Collaborate in a team environment to plan and execute a substantial capstone project, experience and/or piece of scholarship
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 industry-sponsored capstone project experience. Each project is different and has its own individual goals and deliverables.
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.
You will be assessed based on the project deliverables, where you will apply your knowledge and skills to demonstrate autonomy, expert judgement, adaptability and responsibility at an undergraduate level. Effectively responding to sponsor and project manager’s feedback will also be a key factor in the assessment.
Overview of Learning Resources
You will use computer laboratories and relevant software provided by the University.
RMIT will provide you with resources and tools for learning in this course through Canvas and the RMIT Student website.
A list of recommended learning resources will be provided by your lecturer, including books, journal articles and web resources. You will also be expected to seek further resources relevant to the focus of your own learning:
There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the RMIT Student website.
Overview of Assessment
This course has no hurdle requirements.
Assessment item 1: Project plan
Weighting: 15%
This assessment task supports CLOs 1, 2
Assessment item 2: Oral and/or video presentation
Weighting: 10%
This assessment supports CLO 1, 4
Assessment item 3: Project report
Weighting: 40%
This assessment supports CLOs 1, 2, 3, 4, 5
Assessment item 4: Project Deliverables
Weighting: 35%
This assessment supports CLOs 1, 2, 3, 4, 5