Course Title: Applied Research Project

Part A: Course Overview

Course Title: Applied Research Project

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2191

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2011,
Sem 1 2013,
Sem 1 2015,
Sem 1 2016

MATH2191

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 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,
Sem 1 2022,
Sem 2 2022,
Sem 1 2023,
Sem 2 2023,
Sem 1 2024

Course Coordinator: Dr Yan Wang

Course Coordinator Phone: +61 3 9925 2381

Course Coordinator Email: yan.wang@rmit.edu.au

Course Coordinator Location: 15.4.5

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

To enrol in this course, you must have at least successfully completed eight courses offered in your program. It is assumed that you have a wide knowledge of statistical techniques (as covered in the courses taken) and proficiency with statistical software. It is recommended that you take this WIL course in your final semester of the program.


Course Description

The course is about the application of analytics, statistics and operations research in a real-world situation. You will learn how to think about , analyse and interpret data in a broad context. You will get the opportunity to work on authentic industry project through either placement or project within a workplace that are associated with data analytics. You will develop your verbal and written skills, organise the structure of an industrial research problem and learn about professional practice. 

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. 

The WIL can take a broad range of activities including work placements, industry internship, remote industry project and research project.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Teamwork and project management

  • the ability to contribute to professional work settings through effective participation in teams and organisation of project tasks
  • the ability to constructively engage with other team members and resolve conflict.

Communication

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.


On completion of this course, you should be able to: 

  1. Propose and justify solutions to problems both familiar and unfamiliar and identify relevant solutions-focused strategies. 
  2. Construct and express logical arguments and work in abstract or general terms to increase the clarity and efficiency of your analyses. 
  3. Collaborate in a team through interactions with your peers, distinguishing between ethical collaboration, which is strongly encouraged, and plagiarism, which is prohibited. 
  4. Present project updates and a final  report to a variety of audiences using high level oral and written skills. 
  5. Manage your time, balance competing commitments and meet deadlines for both team-based and individual tasks  to be submitted throughout the semester 
       


Overview of Learning Activities

During this course you will undertake the following learning activities: 

  • Conduct a literature review. 
  • Formally define the project requirements and directions based on the available information sources. 
  • Develop action plan and manage group tasks. 
  • Have regular meetings with group members and your supervisors to have ongoing feedback. 
  • Provide feedback to your peers on their projects. 
  • Present project updates and project outcomes to industry contacts and other students. 
  • Write a detailed report of your research project and submit it for review. 


Overview of Learning Resources

RMIT library resources including online access resources will be critical for the early stages.

You will receive ongoing feedback from industry, other students and your supervisor(s) throughout your project

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats


Overview of Assessment

Note: This course has no hurdles

Assessment Tasks

Assessment Task 1: Project Proposal
Weighting 30%
This assessment task supports CLOs 1 to 5.

Assessment Task 2: Project Progress Presentation
Weighting 15%
This assessment task supports CLOs 3, 4 and 5.

Assessment Task 3: Final Presentation
Weighting 15%
This assessment task supports CLOs 3, 4 and 5.

Assessment Task 4: Final Report
Weighting 40%
This assessment task supports CLOs 1 to 5.