Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Business Analytics | |||
| Reference | BS2011 | Version | 3 |
| Created | August 2021 | 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 | Describe the different types of data and information systems found in business and the infrastructure of big data analytics information systems. |
| 2 | Identify the issues,developments and challenges of big data, data analytics and digital technologies. |
| 3 | Discuss the key elements and processes required for the design of a successful big data programme strategy and implementation plan. |
| 4 | Explain how big data and data analytics have the potential to add value within the modern business environment. |
| 5 | Apply tools such as Tableau and Microsoft Power BI to build a visualisation from given data sets. |
| Indicative Module Content |
|---|
| Different types of digital data and information systems; 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 and 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 understanding of students is further enhanced through interactive tutorials. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 36 | N/A |
| Non-Contact Hours | 114 | 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: | 20% | Outcomes Assessed: | 5 |
| Description: | Practical task using data analysis tools. | ||||
| Component 2 | |||||
| Type: | Examination | Weighting: | 80% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Closed book examination. | ||||
| MODULE PERFORMANCE DESCRIPTOR | ||||||||
|---|---|---|---|---|---|---|---|---|
| Explanatory Text | ||||||||
| The calculation of the overall grade for this module is based on 80% weighting of C2 examination and 20% weighting of C1 - coursework components. An overall minimum grade D is required to pass the module. | ||||||||
| Coursework: | ||||||||
| Examination: | A | B | C | D | E | F | NS | |
| A | A | A | A | B | B | E | ||
| B | B | B | B | B | C | E | ||
| C | B | C | C | C | D | E | ||
| D | C | C | D | D | D | E | ||
| E | D | D | D | E | E | E | ||
| F | E | E | E | F | F | F | ||
| 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 | FITZENZ, J. and MATTOX, J., 2014. Predictive analytics for human resources. Hoboken, New Jersey: John Wiley & Sons. |
| 2 | SPONDER, M. and KHAN, G.F., 2017. Digital analytics for marketing. London: Routledge. |
| 3 | STUBBS, E., 2014. Big data, big innovation : enabling competitive differentiation through business analytics. Hoboken, New Jersey : John Wiley & Sons. |
| 4 | WILLIAMS, S., 2016. Business intelligence strategy and big data analytics : a general management perspective. Cambridge, MA: Elsevier. |