Course Title: Industrial Research Methods

Part A: Course Overview

Course Title: Industrial Research Methods

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. 

Please read the Student website for additional requirements of in-person attendance: Coming to campus - COVID protocols for students.

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 Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2010,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015

Course Coordinator: Kaye E. Marion

Course Coordinator Phone: +61 3 9925 3162

Course Coordinator Email:

Course Coordinator Location: 8.9.37

Course Coordinator Availability: See timetable on office door

Pre-requisite Courses and Assumed Knowledge and Capabilities

To enrol in this course, you must have successfully completed at least 4 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 several statistical computer packages.

Course Description

The course is about the application of Statistics in a real world situation. You will learn how to think about data in a broad context and what goes on in a consulting session. You will be taught how to improve your verbal and written skills, organise the structure of a statistical problem and about professional ethics.

Objectives/Learning Outcomes/Capability Development

Some skills you will develop in this course are:

  • Problem solving skills, which include engaging with unfamiliar problems, and identifying relevant strategies.
  • Analytical skills, which include the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of the analysis.
  • The ability to work in a team through interactions with fellow students. The department distinguishes between ethical collaboration, which is strongly encouraged, and plagiarism, which is prohibited.
  • Oral presentation skills, consisting of practising presentation of technical solutions.
  • Time management skills, with both team-based and individually assembled material to be submitted throughout the semester, you will learn to manage your time, balance competing commitments and meet deadline.

This course will address the following Program Learning Outcomes:

  • The ability to apply the principles of lifelong learning to any new challenge.
  • An understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.
  • 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.
  • 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.
  • The ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

Overview of Learning Activities

Face to face seminars with student involvement facilitated.

Overview of Learning Resources

Case studies covering various aspects of statistical consulting will be covered in class with many examples.  These will be available online for students to experience themselves.

Overview of Assessment

Weekly exercises will need to be submitted and will be marked within one week.

Some of this will be for group activities.  The written assessment will be reinforced with class presentations.