Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Business Intelligence | |||
| Reference | CM2138 | Version | 1 |
| Created | December 2023 | SCQF Level | SCQF 8 |
| Approved | June 2019 | SCQF Points | 15 |
| Amended | June 2023 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To provide students with an in-depth knowledge of business intelligence and data warehousing concepts, methods and tools for solving business problems. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Report the main components of a business intelligence system. |
| 2 | Use a methodology for designing a business intelligence solution |
| 3 | Use appropriate tools to create a business intelligence solution. |
| 4 | Compare different methods for data integration and master data management. |
| 5 | Practice different methods of visualising data appropriate to different stakeholders. |
| Indicative Module Content |
|---|
| Business Intelligence (BI) systems and types of decisions managers face. Data Visualisation and Dashboard Techniques. Mapping data to visual representations; awareness of accessibility issues. Data integration, data federation and data virtualisation. Security, privacy and legal issues. Data lakes. ETL (Extraction, Transformation and Loading). Master Data Management. Multi-Dimensional Data Analysis. Concepts and benefits associated with data warehousing. Architecture of a data warehouse. Tools for Data warehousing. |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through lectures and directed reading. In the laboratories the students will progress through a sequence of exercises to further their understanding and gain practical experience of business intelligence and data warehousing techniques and tools. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | N/A |
| Non-Contact Hours | 120 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4, 5 |
| Description: | This coursework will consist of developing a business intelligence and data warehouse solution. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of Component 1. An overall minimum grade D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student must achieve an A in C1. |
| B | The student must achieve a B in C1. |
| C | The student must achieve a C in C1. |
| D | The student must achieve a D in C1. |
| E | The student must achieve an E in C1. |
| F | The student must achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None, in addition to course entry requirements. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | SHERMAN R., 2014. Business Intelligence Guidebook: From Data Integration to Analytics. Morgan Kaufmann. |
| 2 | SHARDA R., DELEN D. and TURBAN E., 2014. Business Intelligence: A Managerial Perspective on Analytics. 3rd ed. Pearson. |
| 3 | KIRK, A., 2016. Data Visualisation, A Handbook for Data Driven Design. Sage Publishing. |
| 4 | VAISMAN, A., 2014. Data warehouse systems: design and implementation. Springer. |
| 5 | DAMA International., 2017. DAMA-DMBOK: Data Management Body of Knowledge. 2nd Ed. Technics Publications. |