Course Title: Minor Thesis

Part A: Course Overview

Course Title: Minor Thesis

Credit Points: 24.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2008,
Sem 1 2009,
Sem 2 2009,
Sem 1 2010,
Sem 2 2010,
Sem 1 2011,
Sem 2 2011,
Sem 1 2012,
Sem 2 2012,
Sem 1 2013,
Sem 2 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016


City Campus


171H School of Science


Sem 1 2017,
Sem 2 2017

Course Coordinator: Assoc. Prof. Melih Ozlen

Course Coordinator Phone: +61 3 9925 3007

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

You should have completed at least 12 courses within your program with at least a GPA of 3.00.

Course Description

This course forms the optional major research component of your postgraduate degree. Below are examples of the topics that you may focus on for your research.  Topics outside of this list may also be acceptable, subject to supervisor availability.

  • Analysis of Categorical Data
  • Analysis of Large Data Sets
  • Applied Bayesian Statistics
  • Data Visualisation
  • Design and Analysis of Experiments
  • Forecasting
  • Game Theory and its Applications
  • Machine Learning
  • Multivariate Analysis Techniques
  • Operations Research
  • Questionnaire and Research Design
  • Regression Analysis
  • Sports Analytics
  • Statistical Inference
  • Statistics of Quality Control and Performance Analysis
  • Stochastic Processes and Applications
  • System Dynamics
  • Systems Simulation
  • Time Series Analysis

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.


  • 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.


  • develop the cognitive skills to review critically, analyse, consolidate and synthesise knowledge to identify and provide solutions to complex problems with intellectual independence.
  • use initiative and judgement in planning, problem solving and decision making in professional practice and/or scholarship.
  • take responsibility and accountability for own learning and professional practice and in collaborations with others within broad parameters.

On completion of this course you should be able to:

  1. Demonstrate research knowledge and skills pertinent to the substantive topic area you have chosen;
  2. Summarise and communicate your results in concise and appropriate oral and written language and present them in a manner which is consistent with the scientific conventions of Statistics/Operations Research;
  3. Critically survey the relevant literature and acknowledge the relationship between your own work and that of others;
  4. State clear research objectives and justify your research methods;
  5. Contribute to existing knowledge of the topic within your discipline.

Overview of Learning Activities

You need to find a thesis topic and a supervisor at least one week before the semester starts. You will meet with your supervisor on a regular basis during the semester. You will perform a subset of the following activities:

  • Reviewing the literature
  • Collecting and cleaning data
  • Analysing data
  • Building models/hypothesis
  • Validating models or testing hypothesis
  • Writing your thesis
  • Presenting your thesis

Overview of Learning Resources

Books and research journals in the RMIT libraries.


Library Subject Guide for Mathematics & Statistics

Overview of Assessment

☒This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Presentation
Weighting 5%
This assessment task supports CLO 2

Assessment Task 2: Thesis
Weighting 95%
This assessment task supports CLOs 1, 2, 3, 4 and 5