Last modified: 23 Jul 2024 11:09
This module introduces students to big data, analytics and visualisation linked to business applications. The module also enables students to solve a variety of complex data-driven business problems using computer software tools, examples R and Gephi. The focus of the module thus is twofold: 1) building relative theoretical understandings, 2) and practice analytics for data-driven decision making for business operations and problems.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Third Term | Credit Points | 30 credits (15 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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The module emphasises two broad theoretical and practical domains, which are specified as follows:
Theoretical understanding: This part provides the theoretical foundations of big data, analytics, network analysis and unstructured data. It further strengthens the effective use of data visualisation techniques, digitisation and their links with business applications. The theoretical foundations are further linked with a variety of business domains such as supply chains, network analytics and operations management.
Practical business applications: Students learn how to explore and solve business problems using R and Gephi software platforms. Students are involved through workshops and they actively solve variety of exercises that prepare them to be data-driven managers and executives capable of utilising data, analytics and digital networks for optimising their business performance.
| Description | Value |
|---|---|
| Guest speakers | 200.0000 |
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
5,000-word individual project. Feedback will be provided within 3 weeks of submission. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Remember | Demonstrate conceptual understanding of big data, analytics, digitisation and visualisation techniques. |
| Factual | Apply | Learn and apply software tools such as R and Gephi. |
| Procedural | Analyse | Utilise a variety of resources and techniques to systematically examine how managers and executives can utilise big data and analytics for business value creation interlinked with their performance. |
| Reflection | Evaluate | Critically evaluate big data, analytics and visualisation aspects to explore and solve business problems. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
5,000-word individual project. Students will not be allowed to work on the same topic as they did for their first attempt. Feedback will be given within 3 weeks of submission. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Remember | Demonstrate conceptual understanding of big data, analytics, digitisation and visualisation techniques. |
| Factual | Apply | Learn and apply software tools such as R and Gephi. |
| Procedural | Analyse | Utilise a variety of resources and techniques to systematically examine how managers and executives can utilise big data and analytics for business value creation interlinked with their performance. |
| Reflection | Evaluate | Critically evaluate big data, analytics and visualisation aspects to explore and solve business problems. |
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Remember | Demonstrate conceptual understanding of big data, analytics, digitisation and visualisation techniques. |
| Procedural | Analyse | Utilise a variety of resources and techniques to systematically examine how managers and executives can utilise big data and analytics for business value creation interlinked with their performance. |
| Reflection | Evaluate | Critically evaluate big data, analytics and visualisation aspects to explore and solve business problems. |
| Factual | Apply | Learn and apply software tools such as R and Gephi. |
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