Module Details
Module Code: |
DIGT H4305 |
Module Title:
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Advanced Data Analysis for Digital Marketing
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Title:
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Advanced Data Analysis for Digital Marketing
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Module Level:: |
8 |
Module Coordinator: |
Myles Kelly
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Module Author:: |
Denise Earle
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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.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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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.
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
Storytelling with Data
Best practices of data visualisation and storytelling with data. Application of these techniques using key software.
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Reports & Dashboards
Design and generation of marketing reports and dashboards using modern data science techniques and tools.
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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.
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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.
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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...
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Module Content & Assessment
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Assessment Breakdown | % |
Continuous Assessment | 100.00% |
AssessmentsFull Time
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.
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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 |
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Contact |
Practicals/labs |
Every Week |
6.00 |
6 |
Independent Learning |
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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 |
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Contact |
No Description |
Every Week |
3.00 |
3 |
Independent Learning Time |
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Non Contact |
No Description |
Every Week |
15.00 |
15 |
Total Weekly Contact Hours |
3.00 |
Module Resources
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Recommended Book Resources |
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Garrett Grolemund,Hadley Wickham. R for Data Science, O'Reilly Media, [ISBN: 1491910399].
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Cole Nussbaumer Knaflic. (2015), Storytelling with Data, 1st. John Wiley & Sons, [ISBN: 1119002257].
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Cole Nussbaumer Knaflic. (2019), Storytelling with Data - Let's Practice, John Wiley & Sons, [ISBN: 1119621496].
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Chris Chapman, Elea McDonnell Feit. (2019), R For Marketing Research and Analytics, Springer, [ISBN: 3030143155].
| Supplementary Book Resources |
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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].
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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].
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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 |
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This module does not have any other resources |
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