Module: Management Science & Business Modelling

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Management Science & Business Modelling
Module Code	ICT1010 (ITS: MS002)
Faculty	Electronic Engineering	School	Engineering & Computing
NFQ level	8	Credit Rating	15

Description

A module which introduces students to topics in management science and mathematical modelling for business, including machine-learning techniques.

Learning Outcomes

1. Describe different types of mathematical models used to solve common business problems
2. Formulate various problems for solving via modeling and/or machine learning
3. Solve modelling algorithms using tables, graphs and calculation
4. Use software, including machine learning, to model problems and derive possible solutions
5. Interpret the output of software when used to solve business and machine learning problems
6. Recommend strategies via written reports based on the results of mathematical modelling of real world problems

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Tutorial	20	Online tutorials are held in DCU on Saturdays and weekday evenings according to the timetable
Online activity	40	Interaction with tutor and fellow students
Assignment Completion	75	Work independently on assessments over the course of the academic year
Assessment Feedback	15	Assimilate and apply individual and general feedback received on each assignment
Independent Study	223	Reading course notes and recommended reading. Researching and studying web resources. Library work. Examination preparation.
Directed learning	2	Examination
Total Workload: 375

Section Breakdown
CRN	11665	Part of Term	Semester 1 & 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Richard Bolger	Module Teacher	Mary Sharp

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Written assignment based on units 1 -5	7%	Week 11
Assignment	Written assignment based on units 1 - 11	9%	Week 19
Assignment	Written assignment based on units 1 - 14	9%	Week 27
Formal Examination	End-of-Semester Final Examination	75%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

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Indicative Reading List

Books:

David R. Anderson, Dennis J. Sweeney and Thomas A. Williams: 2010, An Introduction to Management Science, International Edition, 13th, South Western College,
Eric V. Denardo: 2008, Science of Decision Making: A problem-based approach using Excel: Student edition, John Wiley & Sons,
Taylor, B. W.: 2015, Introduction to Management Science, 12th, Pearson,
Trevor Hastie and Robert Tibshirani: 2011, The Elements of Statistical Learning: Data Mining, Inference, and Prediction., 2nd, Springer,
Gareth James and Daniela Witten: 2016, An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), Springer,
Ian H. Witten and Eibe Frank: 2011, Data Mining: Practical Machine Learning Tools and Techniques, 3rd, Morgan Kaufmann,
Thom M. Mitchell: 1997, Machine Learning (International edition), McGraw-Hill Education,
Negnevitsky, Michael: 2011, Artificial intelligence, Third, Pearson Education Canada,

Articles:
None

Other Resources

None