Module Details

Module Code: DIGT C1805
Module Title: Introduction to Data Analysis for Digital Marketing
Title: Introduction to Data Analysis for Digital Marketing
Module Level:: 6
Credits:: 10
Module Coordinator: Terence Lawless
Module Author:: Denise Earle
Domains: Sport Media and Marketing
Module Description: The aim of this module is to develop students' mathematical and statistical skills with a view to using these skills to analyse digital marketing data. Students will be introduced to the areas of digital marketing data, descriptive statistics, hypothesis testing, correlation and regression. The students will also be introduced to the use of statistical software for data analysis.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Identify and explain basic digital marketing terminology.
LO2 Describe basic concepts in probability, sampling and inference.
LO3 Apply statistical skills and thinking to explore data numerically and graphically.
LO4 Interpret data in Digital Marketing scenarios.
LO5 Solve well-formed problems by selecting the appropriate techniques and presenting the answer 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
Introduction
Introduction to digital marketing data and terminology.
Basic Mathematics
Basic arithmetic operations, calculations involving percentages, fractions and ratios, roots and powers. Apply various techniques to business problems.
Introduction to Statistics
Different data types, tabulation of data, graphical representation of data and sampling. Measures of central tendency and dispersion including mean, median and standard deviation.
Further Statistical Topics
Application of correlation, linear regression, and hypothesis testing in a marketing context (e.g. for A/B testing).
Data Visualisation
Description of different data visualisation techniques, their purpose and when they are suitable to use.
Computer Practicals
Application of theoretical material using relevant computer programs.
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment70.00%
Project30.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Short Answer Questions % of Total Mark 20
Timing Ongoing Learning Outcomes 1,2,3,4,5
Non-marked No
Assessment Description
There will be a series of assignments to offer formative feedback throughout the year.
Assessment Type Examination % of Total Mark 50
Timing Ongoing Learning Outcomes 1,2,3,4,5
Non-marked No
Assessment Description
There will be a series of in-class tests throughout the year in order to assess students' learning.
Project
Assessment Type Project % of Total Mark 30
Timing n/a Learning Outcomes 1,3,4,5
Non-marked No
Assessment Description
The final assessment of the year will be a 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 Non Contact No Description Every Week 15.00 15
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Mik Wisniewski & Farhad Shafti. Quantitative Analysis for Decision Makers, 7th Edition, 7th. [ISBN: 1292276614].
  • Wayne L. Winston. (2014), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, John Wiley & Sons, [ISBN: 111841730].
  • Cole Nussbaumer Knaflic. (2019), Storytelling with Data - Let's Practice, John Wiley & Sons, [ISBN: 1119621496].
  • Les Oakshott. (2016), Essential quantitative methods for business, management and finance, 6th. Palgrave, Macmillan Education, London, [ISBN: 9781137518569].
This module does not have any article/paper resources
Other Resources
Discussion Note: