Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Analytics For Business Decisions | |||
| Reference | CB2336 | Version | 2 |
| Created | February 2024 | SCQF Level | SCQF 8 |
| Approved | July 2018 | SCQF Points | 30 |
| Amended | April 2024 | ECTS Points | 15 |
| Aims of Module | |||
|---|---|---|---|
| This module provides students with an insight into the world of Data and Business Analytics. This includes data analytics processes, data resources, advantages as well as limitations of data analytics. Students will also learn key concepts and terminologies of data analytics, and statistics for informed business decisions. Students will also be introduced to other analytics concepts which include predictive analytics, clustering & segmentation. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Demonstrate a detailed understanding of Data Analytics, its advantages and limitations |
| 2 | Understand the contexts and applications of Data Analytics |
| 3 | Analyse data by applying statistical models and techniques. |
| 4 | Apply data analytics to business problems |
| Indicative Module Content |
|---|
| Understanding data analytics, concepts, terminologies, advantages and limitations; a data-driven strategy to business problems; statistics for business analytics. Understanding and applying the data analytics lifecycle (CRISP-DM) to business problems. The module engages with UNESCO's Education for Sustainable Development Critical thinking, Strategic, Normative and Integrated problem-solving competencies, enabling students to analyse complex systems, question norms, practices and opinions, reflect on their values and perceptions, and apply different problem-solving frameworks to complex problems. |
| Module Delivery |
|---|
| The module is delivered via workshops, case studies, lab tutorials, and online exercises. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 48 | N/A |
| Non-Contact Hours | 252 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 300 | 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: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Individual Portfolio Assessment comprising of an analytics workflow and reflective commentary | ||||
| 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 D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| 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 | THEOBALD, O. (2019): Data Analytics For Absolute Beginners: A Deconstructed Guide to Data Literacy. Independently Published, United States. |
| 2 | THEOBALD, O. (2020): Statistics for Absolute Beginners: A Plain English Introduction. Independently Published, United States. |
| 3 | PROVOST, F. and FAWCETT, T. (2013). Data science for business. Sebastopol, CA: O'Reilly Media |
| 4 | BROWN M. (2014): Data Mining for Dummies. Hoboken, NJ: John Wiley & Sons. |