Module Details

Module Code: DIGT H4305
Module Title: Advanced Data Analysis for Digital Marketing
Title: Advanced Data Analysis for Digital Marketing
Module Level:: 8
Credits:: 10
Module Coordinator: Myles Kelly
Module Author:: Denise Earle
Domains:  
Module Description: The aim of this module is to develop the critical skills required to compile, analyse, statistically model and visualise data using specific tools and techniques.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically reflect on and apply key statistical/visualisation programming tools to analyse marketing data.
LO2 Deliberate on, evaluate and communicate the power of storytelling with data in a digital marketing context and be able to apply this skill using key software.
LO3 Deliberate on, evaluate and communicate the application and creation of predictive analytics in a digital marketing context.
LO4 Deliberate on, evaluate and communicate the application and creation of segmentation modelling in a digital marketing context.
LO5 Deliberate on, evaluate and communicate the application and creation of other advanced data mining techniques (e.g. text analytics) in a digital marketing context.
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
Storytelling with Data
Best practices of data visualisation and storytelling with data. Application of these techniques using key software.
Reports & Dashboards
Design and generation of marketing reports and dashboards using modern data science techniques and tools.
Propensity Modelling
Applications in digital marketing, development using classification trees and regression, assessing quality of propensity models, designing marketing campaigns based on the output of propensity models.
Segmentation Modelling
Applications in digital marketing, development using profiling and cluster analysis techniques, assessing quality of segmentation models, designing marketing campaigns based on the output of segmentation models.
Other Data Mining Techniques
Application of other data mining techniques in a digital marketing context. Techniques may include text analytics, sentiment analysis, market basket analysis, recommendation engines, etc...
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment100.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Other % of Total Mark 100
Timing n/a Learning Outcomes 1,2,3,4,5
Non-marked No
Assessment Description
Learners will be required to demonstrate achievement of the learning outcomes through continuous assessment. This work may take the form of a project (individual/group), practical exam, presentation, case analysis, poster presentation but is not limited to these formats.
No Project
No Practical
No End of Module Formal Examination
Reassessment Requirement
Exam Board
It is at the discretion of the Examination Board as to what the qualifying criteria are.

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
Practicals Contact Practicals/labs Every Week 6.00 6
Independent Learning Non Contact Independent Learning Every Week 12.00 12
Total Weekly Contact Hours 6.00
Workload: Part Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Practicals Contact No Description Every Week 3.00 3
Independent Learning Time Non Contact No Description Every Week 15.00 15
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Garrett Grolemund,Hadley Wickham. R for Data Science, O'Reilly Media, [ISBN: 1491910399].
  • Cole Nussbaumer Knaflic. (2015), Storytelling with Data, 1st. John Wiley & Sons, [ISBN: 1119002257].
  • Cole Nussbaumer Knaflic. (2019), Storytelling with Data - Let's Practice, John Wiley & Sons, [ISBN: 1119621496].
  • Chris Chapman, Elea McDonnell Feit. (2019), R For Marketing Research and Analytics, Springer, [ISBN: 3030143155].
Supplementary Book Resources
  • Foster Provost and Tom Fawcett. (2013), Data Science for Business: What you need to know about data mining and data-analytic thinking, 1st. O'Reilly, United States, [ISBN: 1449361323].
  • Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl, Jr.. (2017), Data Mining for Business Analytics, John Wiley & Sons, [ISBN: 1118879368].
  • Daniel S. Putler, Robert E. Krider. (2012), Customer and Business Analytics, Chapman and Hall/CRC, [ISBN: 1466503963].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: