| Module Title |
Application Domains 3 |
| Module Code |
CSC1112 (ITS: CA4025) |
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Faculty |
Computing |
School |
Engineering & Computing |
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NFQ level |
8 |
Credit Rating |
7.5 |
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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.
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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 |
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| Section Breakdown | | CRN | 20396 | 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 | Sahraoui Dhelim | Module Teacher | Andrew Way (Emeritus Prof), Cathal Gurrin |
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| Assessment 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;
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
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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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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Indicative Reading List
Books:
None
Articles:
None |
Other Resources
None |
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