#REQUEST.pageInfo.pagedescription#

Site Navigation

MATH8009 - Maths Methods and Modelling

banner1
Title:Maths Methods and Modelling
Long Title:Maths Methods and Modelling
Module Code:MATH8009
 
Credits: 5
NFQ Level:Advanced
Field of Study: Mathematics
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 2 programme(s)
Module Coordinator: David Goulding
Module Author: Sean Lacey
Module Description: This module will explore various mathematical techniques and will focus on mathematical models of real world processes, their formulation and methods of solution - both numerical and analytical. Central to the module will be practical problems that arise in industry and commerce.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Formulate and analyse well posed linear, exponential, Markov chain and statistical models.
LO2 Examine linear systems, matrix operations and Gaussian elimination.
LO3 Select and develop numerical methods/algorithms to solve statistical models.
LO4 Write computer programs which yield sensible answers to linear, exponential and statistical models.
LO5 Develop programs to implement numerical algorithms to solve formulated models.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Introduction to modelling
Highlight the pattern in the modelling process. Examine linear and exponential functions – with models.
Linear Algebra
Introduce linear systems, matrices and Gaussian elimination, in addition to covering topics such as matrix operations and inverse of a matrix and linear independence.
Markov chains
Introduce and explore properties of discrete time Markov chains. Demonstrate how effective Markov chains are at modelling practical situations.
Statistical modelling
Derive and model situations using statistical distribution functions, including normal, binomial and Poisson distributions. Explore real world statistical models.
Linear regression analysis
Describe the relationship between two quantitative measurements (scatter plots); the strength of the relationship (correlation coefficient); and model the relationship (simple linear regression).
Software
R, Excel, VBA.
Assessment Breakdown%
Course Work50.00%
End of Module Formal Examination50.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation Mathematical Analysis and Linear Algebra 1,2,4,5 25.0 Week 6
Practical/Skills Evaluation Numerical Methods 1,3,4,5 25.0 Week 12
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3 50.0 End-of-Semester
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 2.0 Every Week 2.00
Lab Software packages 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Review of lecture notes and solving problems from worksheets 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 1.5 Every Week 1.50
Lab Software packages 1.5 Every Week 1.50
Lecturer-Supervised Learning (Contact) Solving problems from worksheets 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Review of lecture notes and solving problems from worksheets 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Gilbert Strang 2016, Introduction to Linear Algebra, 5th Ed., Wellesley-Cambridge Press [ISBN: 9780980232776]
  • Milton Abramowitz, Irene Stegun 2014, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables [ISBN: 9781614276173]
  • Nicolas Privault 2013, Understanding Markov Chains: Examples and Applications, Springer [ISBN: 9814451509]
  • Daniel P. Maki, Maynard Thompson 2006, Mathematical modeling and computer simulation, Thomson Brooks/Cole Belmont, CA [ISBN: 0534384781]
Supplementary Book Resources
  • Gary N. Felder 2015, Mathematical methods for physics and engineering, John Wiley & Sons Cambridge [ISBN: 1118449606]
  • Bennett, J and Briggs, W. 2014, Using and understanding mathematics: A quantitative reasoning approach, 6th Ed., Pearson [ISBN: 1292062304]
  • D. W. Jordan and P. Smith 2008, Mathematical techniques, 4th Ed., OUP Oxford [ISBN: 0199282013]
  • Frank R. Giordano, Maurice Weir 1997, First Course in Mathematical Modeling, 2nd Ed., Brooks/Cole [ISBN: 0534222482]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 1 Mandatory
CR_SDAAN_9 Master of Science in Data Science and Analytics 1 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie