Module: Application Domains 3

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Application Domains 3
Module Code	CA4025 (ITS) / CSC1112 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Sahraoui Dhelim
Module Teachers	Andrew Way, Cathal Gurrin
NFQ level	8	Credit Rating	7.5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only
Reassessment of this module will consist of reassessed coursework element(s).

Description

This module presents students with a series of 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 ethical AI, responsible AI, and green AI.

Learning Outcomes

1. Explain applications of data science and data analytics in 3 different domains (e.g. ethical AI, green AI, responsible AI)
2. Summarise the main issues and challenges for data-driven approaches in the 3 domains
3. Debate the scope of data-driven approaches to major aspects of our lives in the 3 domains
4. Predict potential for other data-driven approaches to major aspects of our lives in other domains

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Online activity	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	No Description
Total Workload: 187

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	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Examine codes of conduct for software engineering and adapt to data science/AI practitioners	20%	Week 23
Extended Essay / Dissertation	Examine how AI systems should be assessed to ensure they are being used for good	50%	Week 26
Assignment	Gather data related to nutrition, physical activity & sleep, and perform data analysis to predict wellbeing	30%	Week 30

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

None