Module Details

Module Code: BUSS H4710
Module Title: Data Analytics (Tourism and Event Management)
Title: Data Analytics (Tourism and Event Management)
Module Level:: 8
Credits:: 5
Module Coordinator: Janette Davies
Module Author:: Edel Glowatz
Domains:  
Module Description: The aim of this module is to provide students with an understanding of data analytics and to critically evaluate, select and utilise data analytics (including statistics) and the insights garnered ethically in a tourism & event management context
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically reflect on, evaluate and communicate the key principles, theories and techniques for data analytics as they apply the tourism and event management sectors
LO2 Demonstrate practical knowledge of Web Analytics tools, Social Media Analytics and Email marketing Analytics
LO3 Appraise and evaluate key business and marketing metrics, KPIs and customer segmentation
LO4 Appraise and analyse data, present and defend it effectively to others
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Concept & current state of data analytics
Evolution & current status of data analytics, business & marketing insights
Types of data (big data, structured, un-structured)
Evaluation of the types of data
Digital marketing metrics and KPIs
Key marketing and business metrics & KPIs
Marketing analytics techniques & tools (e.g.web & social media)
Web analytics (such as google analytics, heat mapping...) Social media analytics (such as facebook insights, Twitter analytics) email marketing analytics
Data exploration
Data exploartion using pivot tables - exploration and visualisation of data
Statistical analysis
Analysis of data using statistical techniques such as regression, correlation
Customer segmentation
Segmenting customers and designing measureable marketing campaigns to target different segments
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment100.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Presentation % of Total Mark 30
Timing n/a Learning Outcomes 2,3,4
Non-marked No
Assessment Description
Students will make a presentation on skills learned to-date
Assessment Type Project % of Total Mark 70
Timing n/a Learning Outcomes 1,2,3
Non-marked No
Assessment Description
This work will take the form of a project where students will be assessed on the practical knowledge of web analytic tools, social media analytics and email marketing analytics
No Project
No Practical
No End of Module Formal Examination

Part Time

Continuous Assessment
Assessment Type Project % of Total Mark 100
Timing n/a Learning Outcomes 1,2,3,4
Non-marked No
Assessment Description
n/a
No Project
No Practical
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lectures 12 Weeks per Stage 3.00 36
Independent Learning Time Non Contact Data Analytics 15 Weeks per Stage 5.93 89
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Data Analytics 12 Weeks per Stage 1.50 18
Independent Learning Time Non Contact Data Analytics 15 Weeks per Stage 2.97 44.5
Total Weekly Contact Hours 1.50
 
Module Resources
Recommended Book Resources
  • Sigala, M., Rahimi, R., Thelwall, M. (Eds.). (2019), Big Data and Innovation in Tourism, Travel, and Hospitality, Springer.
  • Łukasiewicz, K.. (2020), Big Data Analytics in Tourism, Taylor & Francis Group, [ISBN: 9781003057291].
  • Wayne L. Winston. (2014), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, John Wiley & Sons, p.720, [ISBN: 111841730].
  • Rajkumar Venkatesan, Paul Farris, Ronald T. Wilcox. (2014), Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands On Learning, Pearson Education, p.320, [ISBN: 0133552578].
Supplementary Book Resources
  • Minelli, M., Smith, D., Chambers, M.. (2013), Big Data, Big Analytics - Emerging Business Intelligence and Analytic Trends for Today's Businesses, First. Wiley, p.256, [ISBN: 111814760X].
Recommended Article/Paper Resources
  • Journal of Marketing Analytics.
  • International Journal of Business Analytics.
  • Journal of Research in Interactive Marketing.
  • Journal of Service Research.
  • MIT Sloan.
This module does not have any other resources
Discussion Note: