Module: Application Domains 2

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Application Domains 2
Module Code	CA4016 (ITS) / CSC1107 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Ellen Rushe
Module Teachers	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
The continuous assessment course work may be re-taken.

Description

This module presents students with a series of four 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. The four application domains covered in this module are Smart Planning (how various data sources, including open data, are now used in planning the world around us), Health and Human Performance (how data is used to inform experts in fields like Sports Analytics and Medicine), Language Technologies (how access to huge volumes of online information has transformed language processing), and Ethical AI (essentially, 'AI for Good').

Learning Outcomes

1. Explain applications of data science and data analytics in 4 different domains namely Smart Planning, AI for Good, Language Technologies, and Health and Human Performance Analytics.
2. Predict potential for other, data-driven approaches to major aspects of our lives in other domains
3. Summarise the main issues and challenges for data-driven approaches in the 4 domains
4. Debate the scope of data-driven approaches to major aspects of our lives in the 4 domains

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	48	A series of guest lectures from industry and enterprise partners in each of the 4 application domains for this module
Assignment Completion	48	Completion of assignments
Independent Study	91	In order to complete their project, the students will need to consult prior work on the topic, come up with novel solutions (perhaps inc. new datasets) to the problem, evaluate their efforts compared to the state of the art, write a report, and present their work in class at the end of the task.
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

A suite of online resources will be made available through the Loop page for the module.
During this course, a number of expert speakers will explain how and why data gathering and analysis can be of use in a range of application areas. We will focus on Machine Learning/AI applications in: (i) Smart Infrastructural Planning: e.g. Smart Cities, Smart Stadiums, Smart Planning; (ii) Health and Human Performance: Data Science in Medicine, sports analytics etc.; (iii) AI for Good: Ultimately we want to achieve 'Driveable AI', with much more user control; (iv) NLP systems which benefit a range of users. There will be a mix of generic online material, and guest lectures specifically commissioned for this module. Seminars will follow up on the main issues arising from the consumption of this material. Students will decide on a particular task in an application domain based on what they have been exposed to in the module. They will research the specific domain of application and the prior work in that area. Working individually, they will (i) write a project proposal describing this work, (ii) implement the project, write an academic paper on what they did, and describe the results that were obtained, and (iii) present these findings individually via an in-class presentation.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Project	Students will individually compete a number of sub-assessments as part of a larger project. This will consist of the following: A project proposal - Dataset proposal, GDPR/copyright/licence considerations, etc. (10%); An implementation - Data processing, analysis, modelling etc. (30%); A report - in-class written report on analysis etc. (40%); An In-class presentation (15%); A participation self assessment (This is an interactive module so students will be asked to assess their participation backed up by evidence) ( 5%)	100%	n/a

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