Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Tourism Analytics | |||
| Reference | CB3102 | Version | 2 |
| Created | February 2024 | SCQF Level | SCQF 9 |
| Approved | January 2024 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| This module prepares students to understand the principles of effectively using data within both public and private tourism organisations in order to manage and enhance internal and external operations. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Discuss current and future development of data analytics in the tourism industry |
| 2 | Assess the key benefits of analytics to the wider tourism industry |
| 3 | Use analytical techniques with a range of tourism data |
| 4 | Use tourism data sets to produce results of analysis, with a clear explanation of process and outcomes |
| Indicative Module Content |
|---|
| Introduction to tourism data analytics; consumer feedback analysis; tourism data types and sources; tourist trends; Social media Sentiment Analysis for destination branding and perception management; Data preparation and quality; Model Development and training; tourism data visualisation and presentation; storytelling with data. 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 | 36 | N/A |
| Non-Contact Hours | 114 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 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 | ||||
| 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 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 | CHAKRABORTY, G., PAGOLU, M. and GARLA, S., (2014). Text mining and analysis: practical methods, examples, and case studies using SAS. SAS Institute. |
| 2 | RITA, P., RITA, N. and OLIVEIRA, C., (2018). Data science for hospitality and tourism. Worldwide Hospitality and Tourism Themes |
| 3 | YALLOP, A and SERAPHIN, H. (2020). Big data and analytics in tourism and hospitality: opportunities and risks. Journal of Tourism Futures |
| 4 | XIANG, Z. and FESENMAIER, D.R., (2017). Analytics in smart tourism design: concepts and methods. Springer International Publishing Switzerland. |