Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Statistics For Business Analytics | |||
| Reference | CMM723 | 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 in-depth knowledge of the data analytic iterative lifecycle, methodical exploration of an organisation's data with emphasis on specialised statistical analysis through the use of state-of-the-art data analysis tools such as R. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Make informed judgements on data transformation methods for statistical analysis. |
| 2 | Evaluate the analysis methods based on the relevant theories. |
| 3 | Appraise the anomaly detection methods for a given business dataset. |
| 4 | Synthesise conclusions, insights and recommendations to a wider audience by tailoring them at different levels of detail. |
| Indicative Module Content |
|---|
| Statistical techniques for data analysis: random variables, standard deviation, binomial, the central limit theorem, and confidence intervals, hypothesis testing, linear regression and outlier detection, data partitioning, regression trees, logistic regression, clustering. Manipulate structures: matrices, lists, factors, and data frames. Probability: distributions, and random variables. Analysis: calculate statistics and confidence intervals, and perform statistical tests, R Studio applied for business case studies. |
| Module Delivery |
|---|
| This is a lecture based course enhanced through interactive tutorials and directed reading, supplemented with practical lab sessions where data analysis tools will be used to teach students on applying statistical analysis methods for business data. |
| 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 a B in Component 1. |
| C | The student needs to achieve a C in Component 1. |
| D | The student needs to achieve a 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 | ACZEL, A. and SOUNDERPANDIAN, J., 2008. Complete Business Statistics. 7th ed. McGraw-Hill/Irwin. |
| 2 | HYNDMAN, R. and AHANAPOLOULOS G., 2012. Forecasting: Principles and Practice. [online]. Available from: https://www.otexts.org/fpp. |
| 3 | BRIDGELAND D.M. and ZAHAVI R., 2008. Business Modeling: A Practical Guide to Realizing Business Value. Morgan Kaufmann. |
| 4 | FIELD, A., MILES, J. and FIELD, Z., 2012. Discovering Statistics Using R. SAGE Publications. |