Module: Data Management and Visualisation

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Management and Visualisation
Module Code	CSC1175 (ITS: CA682D)
Faculty	Engineering & Computing	School	Computing
NFQ level	9	Credit Rating	10

Description

This module aims to develop an understanding of the management and structuring of large datasets. This module will develop an understanding of critical role of exploratory data analytics, data quality and data governance. Techniques for data visualization, particularly of large datasets, will be discussed and implemented. This module covers issues of data protection and privacy in the context of data visualisation.

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.
6. Outline the key challenges of protecting privacy rights and enforcing the General Data Protection Regulation in the context of data visualisation.
7. Explain different levels of measurement and data types.
8. Distinguish between descriptive and inferential statistical quantities in data analytics.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	No Description
Tutorial	8	No Description
Laboratory	11	Computer-based
Assignment Completion	22	No Description
Independent Study	185	No Description
Total Workload: 250

Section Breakdown
CRN	11963	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. Analyse the potential data privacy issues that could occur in using the data to create a visualisation.	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 Analytics
Basic understanding of descriptive and inferential statistics; Explore and describe datasets; Identify data types.

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

Data Management
Optimizing data management; queries and impact on data management; information lifecycle; mapping, transformation and pre-processing. Data annotation and meta-data.

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

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

Evaluation of visualisation
Impacts of privacy and data protection considerations on data visualisation choices. Understanding of effective communication and best practice for creating data visualisation.

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
Rob Kitchin: 2014, The data revolution: big data, open data, data infrastructures & their consequences, 1446287483
VicenÃ§ Torra: 2017, Data Privacy: Foundations, New Developments and the Big Data Challenge, 9783319573588

Articles:
None

Other Resources

None

This is a version of CA682 with 2.5 extra ECTS delivered specifically to the MDPP programme.