Module: Data Analysis & Visualisation

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Analysis & Visualisation
Module Code	CSC1007 (ITS: CA121)
Faculty	Computing	School	Engineering & Computing
NFQ level	8	Credit Rating	5

Description

The module aims to equip students with an understanding of data analysis and visualisation techniques and the knowledge of a variety of tools and statistical techniques to make sense of the emergence and exponential growth of big data. It will teach students to identify suitable approaches for business related issues.

Learning Outcomes

1. Understand the data analysis pipeline, how it is used in organisations and the ethical implications.
2. Explain the purpose and benefits of data analysis, in particular the emergence of big data.
3. Understand the requirements for communicating data analysis through visualisations and critique both their own and other visualisations.
4. Use functions in spreadsheets such as import, filtering, manipulation, visualisation.
5. Identify and specify requirements (such as tools, data and organisational structure) for performing the analysis of complex business-related issues.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	Lectures and in-class tutorials covering key topics of the course.
Laboratory	12	Laboratory hands-on experience running appropriate software.
Independent Study	89	Preparation for and reading after lectures.
Total Workload: 125

Section Breakdown
CRN	20126	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Claudia Mazo	Module Teacher	Denise Freir, Graham Healy, Vaibhavi Chavan

Type	Description	% of total	Assessment Date
Assessment Breakdown
Group assignment	Report and requirement specifications for business analysis task.	15%	n/a
Assignment	An ongoing series of in-lab assessments of spreadsheet skills and visualisations.	25%	n/a
Formal Examination	n/a	60%	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 analysis
big data, appropriate data selection processes, representative sampling, understanding requirements (data tools, organisation structures etc), using spreadsheets for analysis

Data visualisation
selecting appropriate graph types, making visualisations in spreadsheets, critiquing visualisations

Communication
design rules for visualisation, presenting data visualisations, report writing

Business process understanding
data analysis pipeline and how it is used in organisations

Indicative Reading List

Books:

Few, Stephen: 2012, Show Me the Numbers, 2nd Ed., Analytics Press, 978-097060197

Articles:
None

Other Resources

None