| Module Title |
Data Warehousing & Data Mining |
| Module Code |
CSC1104 (ITS: CA4010) |
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Faculty |
Engineering & Computing |
School |
Computing |
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NFQ level |
8 |
Credit Rating |
7.5 |
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Description
A Data Warehouse is the model or structure that supports data mining and decision support. This module teaches students how to build Data Warehouses by understanding their structures and the concept of multi-dimensional modelling. It also covers Data Mining to teach students how to extract knowledge from data warehouses using three different approaches: clustering, association rule mining and classification.
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Learning Outcomes
1. Be able to build Data Warehouses for different applications types
2. Be able to deploy the Data Warehouse Bus Matrix to create individual data marts.
3. Be able to design a multi-dimensional schema model.
4. Analyse the different strategies and techniques involved in Data Mining, and choose the correct approach for each dataset.
5. Be able to construct and deploy data mining algorithms.
6. Be able to determine the predictive accuracy of data mining algorithms
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| Workload | Full time hours per semester | | Type | Hours | Description |
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| Lecture | 24 | No Description | | Group work | 40 | Construct datasets | | Independent Study | 120 | Build Data Mining algorithms |
| Total Workload: 184 |
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| Section Breakdown | | CRN | 10606 | 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 | Mark Roantree | Module Teacher | |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Group assignment | Create, prepare a dataset suitable for data mining algorithms. | 10% | Week 4 | | Assignment | Develop data mining algorithms to generate a result set. Be able to analyses and write a critique of the results. | 20% | Week 8 | | Formal Examination | End-of-Semester Final Examination | 70% | 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:
- Jiawei Han: 2011, Data Mining: Concepts & Techniques, Morgan Kaufmann,
- Max Bramer: 0, Principles of Data Mining, Springer,
- Ralph Kimball: 0, The Data Warehouse Toolkit, Wiley,
Articles:
None |
Other Resources
None |
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