Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Introduction To Data Analytics And Visualisation | |||
| Reference | CB1012 | Version | 2 |
| Created | February 2024 | SCQF Level | SCQF 7 |
| Approved | October 2018 | SCQF Points | 30 |
| Amended | April 2024 | ECTS Points | 15 |
| Aims of Module | |||
|---|---|---|---|
| To enable students to apply the principles of Data Analytics and Visualisation to inform business processes and decisions. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Evaluate different data analysis techniques in response to a business problem |
| 2 | Appraise different types of data visualisation and the contexts within which they may be applied |
| 3 | Prepare and manage data sets and sources for data visualisation |
| 4 | Apply data visualisation tools and techniques to explore, analyse and present data |
| Indicative Module Content |
|---|
| Introduction to data analytics and the data analytics lifecycle (CRISP-DM). A broad overview of key analytics concepts and principles including descriptive analytics and forecasting. Principles of data visualisation; data preparation; data representation; chart types; data-driven storytelling; dashboard design; ethics of visualisation. The module engages with UNESCO's Education for Sustainable Development Normative and Integrated problem-solving competencies, allowing students to develop the ability to understand and reflect on the norms and values that underlie business actions and decisions; and to negotiate sustainability values, principles, goals, and targets, in a context of working with multiple case-studies and employing problem-solving strategies through data visualisation. |
| Module Delivery |
|---|
| The module is delivered via online exercises, workshops, case studies and lab tutorials. |
| 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 a dashboard and a reflective report | ||||
| 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 | ACHARYA, S. and CHELLAPPAN, S. (2017). Pro Tableau. New York: Apress |
| 2 | FEW, S. (2012). Show Me The Numbers. Burlingame, CA: Analytics Press |
| 3 | FOREMAN, J. (2013). Data Smart: Using Data Science to Transform Information Into Insight. Indianapolis: Wiley |
| 4 | KNAFLIC, C. (2015). Storytelling with data. New Jersey: Wiley |
| 5 | MAYER-SCHONBERGER, V. and CUKIER, K. (2013). Big data. A Revolution that will transform how we live, work and think. London: John Murray |
| 6 | MURRAY, D. (2016). Tableau your data!. Indianapolis: Wiley |
| 7 | PROVOST, F. and FAWCETT, T. (2013). Data science for business. Sebastopol, CA: O'Reilly Media |