| Module Title |
Quantitive Analysis for Business Decisions |
| Module Code |
CSC1041 (ITS: CA3000) |
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Faculty |
Computing |
School |
Engineering & Computing |
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NFQ level |
8 |
Credit Rating |
5 |
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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.
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Learning Outcomes
1. Calculate probabilities of events and calculate expected values
2. Identify different data types
3. Summarise and present data sets
4. Identify optimal strategies under risk
5. Apply Binomial, geometric and normal distributions in straightforward situations.
6. Formulate simple linear regression applications.
7. Obtain point and interval estimates of population parameters
8. Use 'SPSS' or 'R' in description and analysis of business data.
9. 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.
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| Workload | Full time hours per semester | | Type | Hours | Description |
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| 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 |
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| Section Breakdown | | CRN | 10220 | Part of Term | Semester 1 | | Coursework | 0% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | Y | | Resit Category | RC3 | Best Mark | N | | Module Co-ordinator | Luca Rossetto | Module Teacher | Claudia Mazo |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| 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
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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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
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Indicative Content and Learning Activities
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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 |
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