Students will analyse business intelligence requirements and strategies, including the use of data analytics, data mining and data warehousing.
- Learning Outcomes
- 1. Analyse the importance of business intelligence for a range of organisations
2. Evaluate the use of data mining and data warehousing for obtaining business intelligence
3. Critically assess alternative techniques for analysis of big data
4. Recommend and justify a strategy for obtaining and using business intelligence to help
meet the goals of a specific organisation
- - Sources of data, ethical and logistical issues, storage, concurrency and scalability
- Tools and techniques for data analytics, data mining and data warehousing
- Online analytical processing, querying and reporting, knowledge discovery, data
- Learning systems, classifiers, decision trees, neural networks, association rules and
- Identifying correlations, patterns and trends in data
- Decision support systems, management support systems
- Teaching and Learning Strategy
- Teaching and learning methods will involve theoretical and practical classes which may include but are not limited to lectures, class discussions, tutorials, case studies, computer laboratory work, group activities, face-to-face and online activities.
- Assessment Criteria
- In order to receive a passing grade, students must achieve a minimum 40% average over all supervised tests and achieve 50% overall for the module.
- Learning and Teaching Resource
- Wintec Learning Management Systems, Computer Laboratory
An extended reference list will be supplied by the tutor at commencement of the module which will be updated as required