Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Analytics For Business Decision-making | |||
| Reference | CB3050 | Version | 2 |
| Created | October 2022 | SCQF Level | SCQF 9 |
| Approved | March 2020 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| This module prepares students to understand the principles of data and business analytics. Using real-life scenarios, students will learn to apply analytics processes, algorithms and methodologies to business problems; and transform data for making informed business decisions. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Demonstrate an understanding of CRISP-DM and all stages of the Data Mining Life Cycle |
| 2 | Analyse a range of data types |
| 3 | Approach business problems data-analytically |
| 4 | Apply business analytics tools to generate business insights |
| 5 | Present data in an appropriate format for a range of stakeholders |
| Indicative Module Content |
|---|
| Understanding the data analytics and data mining lifecycle (CRISP-DM); the roles and responsibilities in business analytics; data-driven strategy and data preparation. A broad overview of key concepts and principles including: descriptive analytics and predictive analytics. The ability to present data in an appropriate format. |
| Module Delivery |
|---|
| Online Learning. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | N/A | 12 |
| Non-Contact Hours | N/A | 138 |
| 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, 5 |
| Description: | Individual Portfolio Assessment. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The module is assessed by one component: C1 - Coursework - 100% weighting. Module Pass Mark = Grade D | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | Excellent - Outstanding Performance |
| B | Commendable/Very Good - Meritorious Performance |
| C | Good - Highly Competent Performance |
| D | Satisfactory - Competent Performance |
| E | Borderline Fail - Failure Open to Condonement |
| F | Unsatisfactory - Fail |
| 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 | Brown M. Data Mining for Dummies. Hoboken, NJ: John Wiley & Sons; 2014. |
| 2 | Pierson L. Data Science. 2nd edition. Hoboken, NJ: For Dummies; 2017. |
| 3 | Provost F, Fawcett T. Data Science for Business. Beijing: O’Reilly; 2013. |
| 4 | Wendler T, Gröttrup S. Data Mining with SPSS Modeler: Theory, Exercises and Solutions. |
| 5 | Winston W, Albright S. Business Analytics: Data Analysis & Decision Making. 7th edition. Mason: South-Western; 2019. |
| 6 | Acharya S, Chellappan S. Pro tableau: a step-by-step guide: Apress, 2017. |