Module Specifications..
Current Academic Year 2023 - 2024
Please note that this information is subject to change.
| Module Title |
Application Domains 3 |
| Module Code |
CA4025 |
|
School |
School of Computing |
| Module Co-ordinator | Semester 1: Cathal Gurrin
Semester 2: Cathal Gurrin
Autumn: Cathal Gurrin
| | | Module Teachers | Andrew Way
Cathal Gurrin
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NFQ level |
8 |
Credit Rating |
7.5 |
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Pre-requisite |
None |
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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Description
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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.
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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
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| Workload |
Full-time hours per semester |
| Type |
Hours |
Description |
| 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
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Indicative Content and Learning Activities
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| Assessment Breakdown | | Continuous Assessment | 100% | Examination Weight | 0% |
| Course Work Breakdown |
| Type | Description | % of total | Assessment Date |
| 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;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component |
| This module is category 1 |
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Indicative Reading List
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Other Resources
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None |
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Array |
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Programme or List of Programmes
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| DS | BSc in Data Science |
| Timetable this semester: Timetable for CA4025 |
| Archives: | |
