Module Title:Advanced Data Analysis for Digital Marketing
Language of Instruction:English
Credits: 10
NFQ Level:8
Module Delivered In 1 programme(s)
Teaching & Learning Strategies: Formal lectures, group-based activities, class discussion, case studies and lab sessions may be used in the presentation of this module. Relevant notes, examples and resources will be available on Blackboard.
Module Aim: 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 should be able to:
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.
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.

No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
 

Module Content & Assessment

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...
Assessment Breakdown%
Continuous Assessment100.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Other 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. 1,2,3,4,5 100.00 n/a
No Project
No Practical
No End of Module Formal Examination

ITCarlow reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Practicals Every Week 6.00
Independent Learning Every Week 12.00
Total Hours 18.00
Workload: Part Time
Workload Type Frequency Average Weekly Learner Workload
Practicals Every Week 3.00
Independent Learning Time Every Week 15.00
Total Hours 18.00
 

Module Delivered In

Programme Code Programme Semester Delivery
CW_BBDMA_B Bachelor of Science (Honours) in Digital Marketing with Analytics 8 Mandatory