Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Business Analytics | |||
| Reference | CM2127 | Version | 1 |
| Created | February 2024 | SCQF Level | SCQF 8 |
| Approved | July 2019 | SCQF Points | 15 |
| Amended | August 2021 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To enable students to develop an appreciation of how data analytics is used for business decisions and the challenges of designing a data management strategy. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Distinguish the different types of data found in businesses and the infrastructure required to process this data. |
| 2 | Report on the challenges expected when managing and analysing data. |
| 3 | Plan a data mining project to provide value within a business environment. |
| 4 | Use analysis tools to build visualisations from given data sets. |
| Indicative Module Content |
|---|
| Different types of digital data; data sources and how data is captured; models for data management strategy, policies and processes; data visualisation; data storage and databases; the practicalities of data management; the segregation and separation of data for analysis from production data. |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through lectures and directed reading. The content is enhanced and reinforced through interactive labs. |
| 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 |
| Description: | A report that describes the student's data handling process, with appropriate data visualisation. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1 |
| B | The student needs to achieve a B in C1 |
| C | The student needs to achieve a C in C1 |
| D | The student needs to achieve a D in C1 |
| E | The student needs to achieve an E in C1 |
| F | The student needs to achieve an F in C1 |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Agbinya, J. I. (2020) Applied Data Analytics - Principles and Applications. River. |
| 2 | Sahay, A. (2020) Business analytics, volume II: A data driven decision making approach for business. Sterling Forest: Business Expert Press. |
| 3 | Kusleika, D. (2021) Data visualization with excel dashboards and reports. Nashville, TN: John Wiley & Sons. |
| 4 | Gordon, K. (2022) Principles of Data Management: Facilitating information sharing. 3rd edn. Swindon: BCS, The Chartered Institute for IT. |