Module: Application Domains 1

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Application Domains 1
Module Code	CSC1052 (ITS: CA337)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	7.5

Description

This module presents students with a series of three (3) domains in which data analytics have had, or are having, a transformative effect on our lives. Students will emerge with a familiarity and an understanding of how data analytics, visualisation and other aspects of data science are being used to change the world in which we live. Application domains will be chosen based on currently relevant or topical themes and availability of expert guest lectures. Potential options identified for this module are Education (how analytics are and can be used to help students and institutions), Transport (how data from transport demand and transport infrastructure are used to schedule public, and private transport effectively), Human Performance & Sports Analytics (how data analytics is used for amateur and professional sports performance and improved broadcast media), Media analytics (how digital media, especially visual, can be analysed and used in a wide range of application areas) and Agriculture and food production (how data from sources as wide-ranging as weather and environment, to food demand, play a part in modern agriculture practices).

Learning Outcomes

1. 1DDF8C65-E286-0001-4AF2-1F6014303DA0
2. Explain applications of data science and data analytics in 3 different domains (e.g., education, transport, sport, media analytics, or agriculture)
3.
4. 7,6,8
5. 1
6. 1E1483C2-5169-0001-DAC8-17B31AD01557
7. Summarise the main issues and challenges for data-driven approaches in the 3 domains
8.
9. 20,6,10
10. 2
11. 1DDF8C66-BC24-0001-80A3-9AB01B201906
12. Debate the scope of data-driven approaches to major aspects of our lives in the 3 domains
13.
14. 7,8,9,10
15. 3
16. 1DDF8C66-CF11-0001-642D-16E49F70D410
17. Predict potential for other, data-driven approaches to major aspects of our lives in other domains
18.
19. 7,12,21
20. 4

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	36	A series of guest lectures from industry and enterprise partners in each of the 3 application domains for this module
Assignment Completion	36	Completion of assignments
Independent Study	115.5	No Description
Total Workload: 187.5

Section Breakdown
CRN	10234	Part of Term	Semester 1
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Sunder Ali Khowaja	Module Teacher	Andrew Way (Emeritus Prof)

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Implementing Text Classification Models and Explain how text classification models can be applied in the field of education, sport, and media analytics.	5%	Week 3
Assignment	Analysis of Text classification models. Highlighting the issues and challenges related to the text classification models designed in Assignment 1	10%	Week 6
Assignment	Design of a web app for three best text classification models from previous assignments. Critically reflect, which of the models would perform well in the aforementioned domains. Students have to map three models to three domains, one for each. Students have to also provide insights as to why they have mapped the model to a particular domain.	15%	Week 9
Assignment	Students have to highlight ethical and legal requirements for implementing the web app on a production server. Students have to implement at least 3 guard rails in order to comply with the ethical and legal constraints on the web app and explain the societal impact it may incur, accordingly.	20%	Week 12
Formal Examination	This will be a 3-hour written exam	50%	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

Guest Lectures
A series of guest lectures from industry and enterprise partners, will be presented for each of the four application domains covered in this module.

Indicative Reading List

Books:
None

Articles:
None

Other Resources

1: Online resources, A suite of online resources will be made available through the Loop (LMS) for this module,