This module will be taught through practical classes in computer labs. Students will be expected to complete problem sheets to enforce learning. Relevant notes, examples and resources will be available on Blackboard.
Module Aim:
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 should be able to:
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.
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
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.
Assessment Breakdown
%
Continuous Assessment
70.00%
Project
30.00%
Continuous Assessment
Assessment Type
Assessment Description
Outcome addressed
% of total
Assessment Date
Short Answer Questions
There will be a series of assignments to offer formative feedback throughout the year.
1,2,3,4,5
20.00
Ongoing
Examination
There will be a series of in-class tests throughout the year in order to assess students' learning.
1,2,3,4,5
50.00
Ongoing
Project
Assessment Type
Assessment Description
Outcome addressed
% of total
Assessment Date
Project
The final assessment of the year will be a project.
1,3,4,5
30.00
n/a
No Practical
No End of Module Formal Examination
SETU Carlow Campus reserves the right to alter the nature and timings of assessment