Module Title:Introduction to Data Analysis for Digital Marketing
Language of Instruction:English
Credits: 10
NFQ Level:6
Module Delivered In 2 programme(s)
Teaching & Learning Strategies: 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 Assessment70.00%
Project30.00%
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

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 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 2 Mandatory
CW_BBDMA_D Bachelor of Science in Digital Marketing with Analytics 2 Mandatory