Course Title: Data Science Postgraduate Project

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

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: 

Successful completion of COSC2669 Case Studies in Data Science

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.

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

This course contributes to the following Program Learning Outcomes for MC267 Master of Data Science:

Enabling Knowledge

You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will:

• Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology;
• Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;
• Recognise and use research principles and methods applicable to data science.

Problem Solving

Your capability to analyse complex problems and synthesise suitable solutions will be extended as you learn to:

• Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification;
• Apply an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Communication

You will learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to:

• Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.

Research and Scholarship

You will have technical and communication skills to design, evaluate, implement, analyse and theorise about developments that contribute to professional practice or scholarship, specifically you will have cognitive skills to:

• Demonstrate mastery of theoretical knowledge and to reflect critically on theory and professional practice or scholarship;
• Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship.

On completion of this course you should be able to:

1. Use research principles and apply appropriate methods to analyse, theorise and justify conclusions about new situations in data science professional practice and/or research;
2. Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship;
3. Apply mastery of theoretical knowledge and reflect critically on theory and professional practice;
4. 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.

 

Total study hours

To achieve high levels of academic results you are expected to spend an average of 20 hours per week working on the project over 12 to 14 weeks.

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

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 a masters level. Effectively responding to sponsor and project manager’s feedback will also be a key factor in the assessment.

This course has no hurdle requirements.

Early Assessment Task: Specification of data science project scope, deliverables and project plan
Weighting: 15%
This assessment task supports CLOs 1, 2

Final oral and/or video presentation of project outcomes:  
Weighting: 10% 
This assessment supports CLO 4

Final written report on project, including self-reflection and team performance/contribution:
Weighting: 40% 
This assessment supports CLOs 1, 2, 3, 4
Note: if the project is performed as team, then individual marks will be adjusted to reflect individual contribution to team outcomes, as well as performance in the team. 

Presentation, communication and self-management tasks/performance
Weighting: 35%
This task is ongoing and includes the team presentation of the project results, communication with the project sponsor and self-management during the project. You will be required to submit a copy of the work log, git commit logs, meeting agendas/minutes for this assessment, as well as meet regularly with the course manager.
This assessment supports CLOs 1, 2, 3, 4