Module: Data Management & Visualisation

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Management & Visualisation
Module Code	CSC1143 (ITS: CA682)
Faculty	Engineering & Computing	School	Computing
NFQ level	9	Credit Rating	7.5

Description

This module aims to develop an understanding of the management and structuring of datasets. This module will develop an understanding of the critical role of exploratory data analytics, data quality and data governance within a data analytics pipeline. Techniques for data visualization, particularly of large datasets, will be discussed and implemented.

Learning Outcomes

1. Analyse the requirements of applications handling large datasets.
2. Demonstrate an ability to efficiently process a large dataset.
3. Practice data quality and data cleaning measures.
4. Critique data-driven visualisations based on their communication goals and effective use of visualisation methods and techniques.
5. Create effective data-driven visualisations.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	Weekly lectures
Laboratory	11	Lab practicals
Assignment Completion	22.5	Assignment activity
Independent Study	130	Coursework study, independent reading and revision
Total Workload: 187.5

Section Breakdown
CRN	10631	Part of Term	Semester 1
Coursework	25%	Examination Weight	75%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC3	Best Mark	N
Module Co-ordinator	Suzanne Little	Module Teacher

Section Breakdown
CRN	11820	Part of Term	Semester 1
Coursework	25%	Examination Weight	75%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC3	Best Mark	N
Module Co-ordinator	Suzanne Little	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Process (explore, clean, format) two or more datasets to produce an analysis of their composition and create a data visualisation demonstrating communication skills.	25%	Once per semester
Formal Examination	End-of-Semester Final Examination	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

Data Collection
Volume, Velocity, Variety and Veracity; Parsing structured and unstructured data

Data Management
Understanding requirements for data management; queries and impact on data management; an data-driven information lifecycle; mapping, transformation and pre-processing, data annotations and metadata.

Data Quality
Accuracy; Completeness; Relevance; Consistency across data sources; Reliability; Accessibility

Data Visualisation
What is data visualisation? Visualisation basics; Traditional forms of data visualisation; Visualising multi-dimensional data; Visualising large datasets (geo-spatial data, temporal data); Interactive visualisation. Use of a data visualisation tool or platform to create data driven visualisations.

Evaluation of Visualisation
Understanding of effective communication and best practice for creating data visualisations.

Indicative Reading List

Books:

Andy Kirk: 2016, Data Visualisation: A Handbook for Data Driven Design, 9781473912144
Cole Nussbaumer Knaflic: 2015, Storytelling with Data: A Data Visualization Guide for Business Professionals, 1119002257
Field Cady: 2017, The Data Science Handbook, 9781119092919

Articles:
None

Other Resources

None