Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Business Modelling and Analytics | |||
| Reference | CMM726 | Version | 3 |
| Created | February 2024 | SCQF Level | SCQF 11 |
| Approved | April 2017 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To provide an understanding of the main underlying principles of business modelling of real-world business data. To also provide specialised knowledge and valuable insights into statistical techniques that are at the forefront of business modelling. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Evaluate suitable tools to model business data, identify anomalies and apply forecasting techniques for complex business problems. |
| 2 | Generate strategies to deal with uncertainty in business data using statistical techniques. |
| 3 | Evaluate the advantages and disadvantages of applying a specific data mining technique to a given business task. |
| 4 | Synthesise a data mining technique to a given business problem. |
| Indicative Module Content |
|---|
| Predictive analytics for management: Monte Carlo analysis, decision trees, regression, probability and bayes theorem, neural network models, forecasting and nearest neighbour retrieval. Basic data mining concepts: attribute selection methods and evaluation methodologies. Case study: use of a data mining tool (SPSS and other popular commercial data miners) applied to a business problem. |
| Module Delivery |
|---|
| This is a lecture based course enhanced through interactive tutorials and directed reading, supplemented with lab sessions where suitable state-of-the-art data modelling tools will be used to further their understanding. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | N/A | 48 |
| Non-Contact Hours | N/A | 102 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | N/A | 150 |
| 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: | Coursework submission worth 100% of total module assessment. | ||||
| 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 of D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in Component 1. |
| B | The student needs to achieve an B in Component 1. |
| C | The student needs to achieve an C in Component 1. |
| D | The student needs to achieve an D in Component 1. |
| E | The student needs to achieve an E in Component 1. |
| F | The student needs to achieve an F in Component 1. |
| 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 | EVANS, J.R., 2015. Business Analytics. Pearson. |
| 2 | ALBRIGHT, C.S. and WINSTON W.L., 2014. Business Analytics: Data Analysis & Decision Making. 5th ed. Cengage Learning. |
| 3 | COVINGTON D., 2015. Analytics: Data Science, Data Analysis and Predictive Analytics for Business (Algorithms, Business Intelligence, Statistical Analysis, Decision Analysis, Business Analytics, Data Mining, Big Data). Amazon Digital Services. |
| 4 | TENNENT. J. and FRIENDS G., 2011. Guide to Business Modelling. Wiley. |