| Short Title: | Data Warehousing & Data Mining |
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| Full Title: | Data Warehousing & Data Mining |
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| Description: | This module aims:
1. To inform students to the potential of data warehousing, online data analysis, and data mining in a marketing environment;
2. To enable students to identify the implications of different database design approaches on the effective of data warehousing applications;
3. To produce students capable of applying data warehousing and data mining techniques in an effective and result-oriented manner in a corporate environment.
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| Learning Outcomes: |
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| On successful completion of this module the learner will be able to | - Identify the features and capabilities of data warehousing for supporting advanced marketing data analysis
- Assess the suitability of different database design approaches to the needs of data warehousing applications.
- Describe the different types of analytical tools available to perform online analysis against corporate data warehouses (and sub-sets).
- Explain the nature of data mining, and identify the potential of various data mining tools and techniques to support market-related analysis of corporate data.
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Module Content & Assessment| Assessment Breakdown | % |
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| Course Work | 60% | | End of Semester Formal Examination | 40% |
| | Outcome addressed | % of total | Assessment Date |
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| Formal End-of-Semester Examination | None | 40% | Semester End |
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| Coursework Breakdown |
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| Type | Description | Outcome addressed | % of total | Assessment Date |
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| Continuous Assessment | Two formal in-class tests, mid and end semester, in which the student will complete an assignment in class, to test the achievement of learning outcomes for the practical work to date | | 30 | n/a | | Continuous Assessment | Marks will be awarded for regular individual and group assignments undertaken during weekly lab sessions (some of which may require completion by a subsequent session). | | 20 | n/a | | Continuous Assessment | Theory quizzes: one or more quizzes on the theoretical aspects of the course will be conducted during the semester, to assess, and provide formative feedback on, each student’s progress towards meeting the course’s learning outcomes. | | 10 | n/a |
IT Tallaght reserves the right to alter the nature and timings of assessment Module Workload & Resources| Workload | Full-time |
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| Type | Description | Hours | Frequency | Average Weekly Learner Workload |
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| Lecture | Data Warehousing: Origins of data warehousing; what a data warehouse is; principles for data warehouse development; sources of data for DW, data integrity issues and data cleansing; data marts. | 0 | Every Week | 0.00 | | Lecture | Database design for data warehousing: Type of analysis & performance issues; database design options – relational vs. multidimensional databases models; star-shaped schema; processing speeds and response times. | 0 | Every Week | 0.00 | | Lecture | Online data analysis: Online analytical processing (OLAP); Multi-dimensional OLAP (MOLAP) and Multi-dimensional databases; Hybrid OLAP (HOLAP), Relational OLAP (ROLAP). | 0 | Every Week | 0.00 | | Lecture | Data Mining: searching for trends and patterns; analysis techniques & algorithms – cluster/segment analysis, artificial intelligence, neural networks; customer relationship management (CRM). | 0 | Every Week | 0.00 | | Total Weekly Learner Workload | 0.00 | | Total Weekly Contact Hours | 0.00 |
| Resources |
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| Required Book Resources |
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- Marakas, George M. 2002, Modern Data Warehousing, Mining and Visualization: Core Concepts, Prentice Hall.
| | Recommended Book Resources |
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- Miller, Thomas W. 2004, Data and Text Mining, Prentice Hall
| | Recommended Article/Paper Resources |
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- Journal of Database Management (JDM), Information Resources Management Association
- Journal of Interactive Marketing, Wiley Periodicals, Inc
| | Other Resources |
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- Various: On-line references and web based support materials
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