Module: Quantitive Analysis for Business Decisions

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Quantitive Analysis for Business Decisions
Module Code	CSC1041 (ITS: CA3000)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	5

Description

This module introduces students to the basic tools of statistical and OR quantitative techniques. Students are introduced to different data types and will be able to identify the methods of analysis for these data. In addition, concepts of probability are introduced enabling students to analyse problems of decision making under risk. Linear regression applications are introduced by way of problem formulation and solution using graphical methods and analysis. Elementary methods of analysis statistical inference in management are also included. Introduction to linear programming is included as well.

Learning Outcomes

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2. Calculate probabilities of events and calculate expected values
5. 1
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7. Identify different data types
10. 2
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12. Summarise and present data sets
15. 3
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17. Identify optimal strategies under risk
20. 4
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22. Apply Binomial, geometric and normal distributions in straightforward situations.
25. 5
26. 1E49A0C7-0373-0001-DB22-AE101DF51EEC
27. Formulate simple linear regression applications.
30. 6
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32. Obtain point and interval estimates of population parameters
35. 7
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37. Use 'SPSS' or 'R' in description and analysis of business data.
40. 8
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42. Formulate simple linear programming problems and solve them graphically; be able to apply a tool such as 'ampl' to set up and solve linear programming problems.
45. 9

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	2 hours a week
Laboratory	12	1 hour a week
Independent Study	59	study of lecture material and exam preparation
Independent Study	30	project work
Total Workload: 125

Section Breakdown
CRN	10220	Part of Term	Semester 1
Coursework	25%	Examination Weight	75%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC3	Best Mark	N
Module Co-ordinator	Luca Rossetto	Module Teacher	Claudia Mazo

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Laboratory sessions are scheduled for this module and some of these will be used to test proficiency in software tool use (e.g. SPSS or R, ampl). More extended project work will also be assigned, including preparation of report(s) and class presentation(s).	25%	n/a
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

Indicative Reading List

Books:

Terence Lucey: 2002, Quantitative Techniques, Burns & Oates, 588, 0-8264-5854-8
Jane M Horgan: 2009, Probability with "R" an Introduction with Computer Science applications, First Ed., All, Wiley New York,
Mark Berenson and David Levine: 1996, Basic Business Statistics, Prentice hall,
Robert Fourer,David M. Gay,Brian W. Kernighan: 2003, AMPL, Duxbury Press, 517, 0534388094
Hamdy A. Taha,Turkey: 0, Loi relative à la Banque centrale de la République de Turquie, 0132729156

Articles:
None

Other Resources

None