Module: Data Analytics for Marketing Applications

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Analytics for Marketing Applications
Module Code	CA259 (ITS) / CSC1027 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Lili Zhang
Module Teachers	Alan Smeaton
NFQ level	8	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Repeat examination

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

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 Assessment	40%	Examination Weight	60%

Type	Description	% of total	Assessment Date
Course Work 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

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

Other Resources

30410, Online resources available through Loop, 0, Online resources,