DCU Home | Our Courses | Loop | Registry | Library | Search DCU

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Analytics for Marketing Applications
Module Code CA259
School School of Computing
Module Co-ordinatorSemester 1: Alan Smeaton
Semester 2: Alan Smeaton
Autumn: Alan Smeaton
Module TeachersAlan Smeaton
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Repeat examination

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.

Workload Full-time hours per semester
Type Hours Description
Lecture24No Description
Laboratory12No Description
Assignment Completion48No Description
Independent Study41No Description
Total Workload: 125

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

Assessment Breakdown
Continuous Assessment40% Examination Weight60%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Digital ProjectStudents 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
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List

    Other Resources

    30410, Online resources available through Loop, 0, Online resources,
    Programme or List of Programmes
    EBCBA in Global Business (Canada)
    EBFBA in Global Business (France)
    EBGBA in Global Business (Germany)
    EBSBA in Global Business (Spain)
    EBTBA in Global Business (USA)
    MINTBSc Marketing, Innovation & Technology

    My DCU | Loop | Disclaimer | Privacy Statement