Course Title: Industrial Research Methods

Part A: Course Overview

Course Title: Industrial Research Methods

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2307

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 1 2018,
Sem 1 2019

Course Coordinator: Dr Yan Wang

Course Coordinator Phone: +61 3 9925 2381

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

Course Coordinator Location: 8.9.34


Pre-requisite Courses and Assumed Knowledge and Capabilities

It is recommended students are familiar with elementary statistics knowledge and basic programming and data management skills.


Course Description

 

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.

This course can take the form of a broad range of activities including group or individual projects, work placements, simulated work placements or a combination of the formers. You will be introduced to the role of analytics in a global context, through participating in a selection of the following: site visits, study tours, seminars, workshops, projects and placements. You will gain practical knowledge and understanding of the contribution that data and analytics can make to the developing world. Exposure to international problems and the opportunity to interact with local and offshore practitioners will contribute significantly to your teaching and learning experience.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.)


Objectives/Learning Outcomes/Capability Development

 

This course contributes to the following Program Learning Outcomes for BH119 Bachelor of Analytics:

Problem Solving

  • cognitive skills to review critically, analyse, consolidate and synthesise knowledge to identify and provide solutions to complex problems with intellectual independence.

Teamwork and Project Management

  • the ability to contribute to professional work settings through effective participation in teams and organisation of project tasks.

Communication

  • communication skills to present clear and coherent exposition of knowledge and ideas to a variety of audiences.

Information Literacy

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

Ethics

  • the ability to reflect on experience and improve your own future practice


 

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, demonstrating respect for your fellow students
  4. Present solutions to technical problems 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

You will participate in a selection of the following: site visits, study tours, seminars, workshops, projects and placements, under the supervision of both the academic staff and the supervisor of a partner institution representative (for work placement). You will be expected to report, verbally and in written form, on your experiences and the results of your investigations.


Overview of Learning Resources

 

Blackboard will be used to give access to general supporting materials (readings, videos, web link) and to discussion forums.

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

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

A library guide is available at http://rmit.libguides.com/mathstats


Overview of Assessment

 

Assessment Tasks:

 

Assessment Task 1:  Seminar Reflection

Organised in the frame of the industry seminar

Weighting 15%

This assessment supports CLOs 1, 2, 3, 4, 5

 

Assessment Task 2  Presentation

At the Student Conference (seminar day) to their peers and industry contacts, receiving feedback and/or at the partner’s institution.

Weighting 20%

This assessment supports CLOs 1, 2, 3, 4, 5

 

Assessment Task 3:  Self Reflection

A short self-reflexion about what you have learnt and self-evaluation

Weighting 5%

This assessment supports CLOs 1, 2, 3, 4, 5

 

Assessment Task 4:  Written report.

Both the content and soft skills will be assessed by a professional panel

Weighting 60%

This assessment supports CLOs 1, 2, 3, 4, 5