Modelling and Simulation
- Sciences
- 400 level
- 3 credit units
- 228 pages
- 20 units
This course introduces the fundamental concepts of modelling and simulation, covering various stages of model development and simulation implementation. It explores different simulation methods and their applications in diverse fields. The course also incorporates essential statistical knowledge, including statistical distributions and probability theories, to enhance understanding of simulation outcomes. Topics such as queuing theory, simulation languages, stochastic processes, and random walks are covered, providing a comprehensive overview of modelling and simulation techniques.
About this course
- Difficulty
- Intermediate
- Study hours
- 96 hours
- Maths
- Intermediate
- Content
- Theoretical, practical, case study, problem solving
- Practical work
- Yes
- Basic Statistics
- Introduction to Programming
- Assignments
- Tutor Marked Assessments (TMAs)
- Final Examination
What you'll read
The real module and unit structure of CIT412, taken from the course material NOUN publishes.
One paragraph, so you can see how it reads
CIT412 · Unit 1: Basics of Modelling and Simulation
Models in science are often theoretical constructs that represent any particular thing with a set of variables and a set of logical and or quantitative relationships between them. Models in this sense are constructed to enable reasoning within an idealized logical framework about these processes and are an important component of scientific theories.
What you should be able to do
- Define and differentiate between various types of models.
- Apply random number generation techniques in simulations.
- Utilize Monte Carlo methods to solve complex problems.
- Analyze statistical distributions and their applications in simulations.
- Construct and analyze queuing models.
- Implement simulation models using appropriate simulation languages.
What it prepares you for
- Simulation Engineer
- Data Scientist
- Operations Research Analyst
- Systems Analyst
- Business Intelligence Analyst
- Manufacturing
- Telecommunications
- Finance
- Healthcare
- Logistics
- QBasic
- FORTRAN
- SIMSCRIPT II.5
- ACSL
- Simulink
Where it gets hard
The units students slow down on, and what makes each one heavy.
- Module 1: MODELLING AND SIMULATION CONCEPTS
Unit 3: Congruential Random Number Generator
Requires strong programming skills and understanding of mathematical concepts.
- Module 1: MODELLING AND SIMULATION CONCEPTS
Unit 5: Statistical Distribution Functions
Involves complex mathematical derivations and abstract concepts.
- Module 3: QUEUES
Unit 3: Queuing Models
Requires understanding of probability distributions and queuing formulas.
A suggested way through it
13 weeks, about 44 hours in total. Yours will differ.
- Week 1Module 1: MODELLING AND SIMULATION CONCEPTS
Unit 1: Basics of Modelling and Simulation · 2 hours
Understand the definitions of model, modelling, and simulation.. Explain the modelling process and its advantages.. Identify different types of models and their applications..
Unit 2: Random Numbers · 2 hours
Describe pseudorandom number generation techniques.. Use the RND function in QBasic to simulate randomness.. Explain the properties of a good random number generator..
- Week 2Module 1: MODELLING AND SIMULATION CONCEPTS
Unit 3: Congruential Random Number Generator · 3 hours
Explain the congruential method for generating random numbers.. Choose appropriate parameters for the congruential method.. Translate the method into computer programs..
- Week 3Module 1: MODELLING AND SIMULATION CONCEPTS
Unit 4: Monte Carlo Methods · 2 hours
Describe the Monte Carlo method and its applications.. Trace the origin of the Monte Carlo method.. Apply Monte Carlo methods to solve problems..
Unit 5: Statistical Distribution Functions · 2 hours
Define statistics and statistical distributions.. Compute measures of central tendency and variations.. Explain the components of statistical distributions..
- Week 4Module 1: MODELLING AND SIMULATION CONCEPTS
Unit 6: Common Probability Distributions · 3 hours
Explain the role of probability distribution functions in simulations.. Describe probability theory and its fundamental concepts.. List common probability distributions..
- Week 5Module 2: MODELLING AND SIMULATION CONCEPTS
Unit 1: Simulation and Modelling · 3 hours
Explain what simulation is and why it is needed.. Describe how simulations are done and various types of simulations.. Give examples of simulation and its areas of application..
- Week 6Module 2: MODELLING AND SIMULATION CONCEPTS
Unit 2: Modelling Methods · 3 hours
Define modelling and describe basic modelling concepts.. Differentiate between visual and conceptual models.. Explain the characteristics of visual models..
- Week 7Module 2: MODELLING AND SIMULATION CONCEPTS
Unit 3: Physics-Based Finite Element Model · 3 hours
Define Finite Element Method (FEM) and its relationship to Finite Element Analysis.. Describe the basics of FEM, including discretization and assembly procedures.. Explain the application of boundary conditions in FEM..
- Week 8Module 2: MODELLING AND SIMULATION CONCEPTS
Unit 4: Statistics for Modelling and Simulation · 3 hours
Define data modelling and describe different types of data models.. Explain the three perspectives of data models.. Provide an overview of database models..
- Week 9Module 3: QUEUES
Unit 1: Simple Theories of Queues · 3 hours
Define queuing theory and describe queuing systems.. Explain basic probability theories in queuing.. Describe essential queuing theories..
- Week 10Module 3: QUEUES
Unit 2: Basic Probability Theories in Queuing · 3 hours
Explain the role of exponential and Poisson probability distributions in queuing systems.. Describe the input and output processes in queuing systems.. Explain steady-state probability for queues..
- Week 11Module 3: QUEUES
Unit 3: Queuing Models · 3 hours
Define queuing models and describe their construction.. Describe single-server queue systems.. Explain multiple and infinite server systems..
- Week 12Module 3: QUEUES
Unit 4: Queuing Experiments · 3 hours
Apply queuing theory in car wash and salesman call scenarios.. Translate queuing experiments into simulation flowcharts and programs.. Simulate goods production using queuing models..
- Week 13Final Revision
Final Revision · 6 hours
Review course materials and prepare for assignments.. Work on tutor-marked assignments (TMAs)..
Preparing for the exam
- Review all module objectives and key concepts.
- Practice solving numerical problems from each unit.
- Create concept maps linking different simulation methods.
- Focus on understanding queuing theory formulas and their applications.
- Practice coding simple simulation models in QBasic or other languages.
- Review all TMAs and their solutions.
- Allocate equal time to each module during exam preparation.
- Create flashcards for key terms and definitions.
- Form a study group to discuss challenging concepts.
- Practice time management during mock exams.
Questions students ask about this course
What is CIT412 about?
This course introduces the fundamental concepts of modelling and simulation, covering various stages of model development and simulation implementation. It explores different simulation methods and their applications in diverse fields. The course also incorporates essential statistical knowledge, including statistical distributions and probability theories, to enhance understanding of simulation outcomes. Topics such as queuing theory, simulation languages, stochastic processes, and random walks are covered, providing a comprehensive overview of modelling and simulation techniques.
How many units does CIT412 have?
CIT412, Modelling and Simulation, has 20 units across 5 modules, over 228 pages of course material. You can read it one unit at a time.
How many credit units is CIT412?
CIT412 carries 3 credit units, at 400 level in Sciences.
Is CIT412 hard?
CIT412 is rated intermediate level, with intermediate mathematical content. It is mostly theoretical, practical, case study and problem solving work, and it has a practical component.
How long does CIT412 take to study?
About 96 hours of study, spread across its 20 units.
How is CIT412 assessed?
CIT412 is assessed by Assignments, Tutor Marked Assessments (TMAs) and Final Examination.
What do I need before starting CIT412?
Basic Statistics Introduction to Programming
What can I do with CIT412?
Simulation Engineer, Data Scientist, Operations Research Analyst, Systems Analyst and Business Intelligence Analyst.