Course Title: Systems Simulation

Part A: Course Overview

Course Title: Systems Simulation

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2219

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2012,
Sem 1 2014,
Sem 2 2015

MATH2219

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2017

Course Coordinator: Assoc Prof. Sergei Schreider

Course Coordinator Phone: +61 3 9925 3223

Course Coordinator Email: sergei.schreider@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge: MATH1324 Introduction to Statistics or equivalent.


Course Description

 

This course provides you with the opportunity to learn about many fundamental aspects of simulation. The content covered will enable you to understand, model and analyse real life systems using statistically-based computer simulation models.

Topics covered include: systems modelling, input analysis, random number generation, statistical output analysis and usage of specialist simulation software.


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:

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

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.

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.


 

On completion of this course you should be able to:

  1. Conceptualise and identify parameters for a framework to model a real-life system
  2. Construct a model of a real-life system using simulation
  3. Analyse the output of a simulation
  4. Solve extended practical problems by broadening simulations
  5. Demonstrate a high level of competence using simulation software
  6. Communicate your findings to your peers.


Overview of Learning Activities

 The course features a range of learning activities that actively engage you in content acquisition and skills development required to conduct simulations of real life systems.

These activities include lectures, practical classes, and assignments related to the usage of simulation software. You will attend lectures where the underlying theory will be presented. Practical computer classes reinforce the material covered in lectures, enhanced by the use of simulation software. Assignments will provide you with an opportunity to practise your problem solving skills, test your understanding and exchange ideas with others. You will also have the opportunity to discuss your progress with teaching staff.


Overview of Learning Resources

 You will have access to simulation software available in the School through myDesktop. This course is taught through a combination of classroom instruction,  exercises and assignments.

You will have access to extensive course materials made available through myRMIT, including lecture notes, a detailed study program, external internet links and access to RMIT Library online and hardcopy resources.

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


Overview of Assessment

 This course has no hurdle requirements.

Assessment Tasks:

Assessment Task 1: Assignments
Weighting 20%
This assessment task supports CLO 1, 2, 3, 4, 5, 6

Assessment Task 2: Mid-semester Test
Weighting 30%
This assessment supports CLOs 1, 2, 3, 4, 5

Assessment 3: Final Exam
Weighting 50%
This assessment supports CLOs 1, 2, 3, 4, 5