Module: Final Year Project

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Final Year Project
Module Code	CSC1108 (ITS: CA4021)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	15

Description

Students focus on applying the skills gained in a variety of problem domains, in a large scale individual project involving an extended analysis of a substantial data set under the direction of a member of academic staff. This investigation is carried out in such a way that it is thorough and detailed and will involve some element of independent analysis of findings. The investigation will be reported through documentation and a final presentation of results.

Learning Outcomes

1. Systematic Design & Proposal: Critically evaluate a complex domain to propose and design a data-driven solution involving the extended analysis of substantial, high-dimensional data sets.
2. Professional Communication & Adaptation: Communicate project technicalities and progress effectively in oral and written formats, adapting the complexity and tone to suit the profile of both technical supervisors and non-specialist stakeholders (DACS 3:Communicate effectively in oral formats, adapting to audience profile as required)
3. Active Collaboration & Listening: Demonstrate professional interpersonal skills within a team environment, including active listening, conflict resolution, and the integration of peer feedback to enhance project outcomes (DACS 5: Demonstrate the effective use of digital technology for communication)
4. Advanced Implementation & Validation: Implement a large-scale, robust data system using industry-standard tools, demonstrating the ability to validate results against defined research objectives or KPIs.
5. Ethical & Professional Responsibility: Critically assess the ethical implications, data privacy requirements (e.g., GDPR), and professional standards associated with the collection, storage, and analysis of the chosen data set.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Independent Study	90	Guided readings, resources, online tutorials from on open source AI communities
Meeting with Supervisors	15	Biweekly or as needed meetings with supervisors
Group work	270	Project Work
Total Workload: 375

Section Breakdown
CRN	10608	Part of Term	Semester 1 & 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC2	Best Mark	N
Module Co-ordinator	Martin Crane	Module Teacher	Marija Bezbradica, Suzanne Little, Tomas Ward

Type	Description	% of total	Assessment Date
Assessment Breakdown
Report(s)	Submission of a detailed proposal which includes a presentation	10%	Once per semester
Practice assessment record	Fortnightly progress reports/meetings with supervisor and maintain a blog of progress as well as repository or research materials (code and date)	10%	Every Second Week
Research Paper	Submission of project research paper, materials, data, code documentation and results. This includes a live demonstration of same and project interview.	80%	Sem 2 End

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

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None