Module:

Latest Module Specifications

Current Academic Year 2025 - 2026

No Banner module data is available

Module Title
Module Code	(ITS: CA259)
Faculty		School
NFQ level		Credit Rating

Description

This module will familiarise the student with basic data analytics techniques as used in present and future-generation marketing. It will cover the basic statistical approaches as well as the data analytics pipeline of data acquisition, cleansing, storage, mining, actioning, and visualisation. It will include hands-on access to some aspects of this pipeline as well as the use of advanced analytics including psychometric profiling and machine learning (both supervised and unsupervised)

Learning Outcomes

1. Understand the importance of the data analytics pipeline, particularly as applied to marketing applications.
2. Understand how various machine learning applications operate, covering both supervised and unsupervised and also ranging into coverage of deep learning.
3. Understand various aspects of data visualisation as an output from an analytics process, including what makes good, bad and indifferent visualisation
4. Gained experience in creating good (and bad) data visualisations.
5. Understand psychometrics and personality profiling, how it works, its pros and cons.
6. Have an understanding of the importance and potential use for advanced marketing analytics developments, especially the use of psychometric profiling.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	No Description
Laboratory	12	No Description
Assignment Completion	48	No Description
Independent Study	41	No Description
Total Workload: 125

Type	Description	% of total	Assessment Date
Assessment Breakdown
Digital Project	Students will be required to submit a series of visualisations of data throughout the semester, which they may create by working in small groups or individually.	25%	n/a
Formal Examination	n/a	75%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Indicative Reading List

Books:
None

Articles:
None

Other Resources

Online resources available through Loop: Online resources,