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

Course Coordinator: Associate Professor James Thom

Course Coordinator Phone: +61 3 9925 2992

Course Coordinator Email: james.thom@rmit.edu.au

Course Coordinator Location: 14.9.16


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite/Co-requisite: COSC2669 Legal, Ethical and Policy Issues in Data Science is a required requisite for this course and should be completed prior to or in conjunction with COSC2667

 

Furthermore, you may not enrol in this course unless it is explicitly listed in your enrolment program structure.


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 corporate 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

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


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.


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 make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email.

Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.


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.

Assessment tasks

 

Early Assessment Task: Specification of data science project scope and deliverables

Weighting 15%

This assessment task supports CLOs 1,2

Feedback from project sponsor/supervisor on progress throughout project: 

Weighting 15%

This assessment task supports CLOs 1,2,3

Final oral and/or video presentation of project outcomes:  

Weighting 20% 

This assessment supports CLOs 4

 

Final written report on project:

Weighting 50% 

This assessment supports CLOs 1,2,3,4