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This Programme is aimed at students who wish to develop a career or pursue further studies in Computer Science and Data Science. We value and therefore encourage our students to be active, motivated, autonomous learners who have a critical and reflective approach to Computer Science and Data Science. We aim to provide a learning environment that will encourage students to learn and practice skills in Computer Science and data analytics, individually and as part of teams. Practicals, tutorials and assignments are key elements of the design of the Programme. As part of this approach to learning, the Programme uses teaching, learning and assessment approaches such as tutorials, practicals, assignments and individual and team projects, as well as traditional lectures, in the design and delivery of the curriculum.
Graduates with training in Computer Science with Data Science work in fields such as:
• Banking and Financial Services
• Consultancy (e.g. Accenture, Deloitte, PwC)
• Internet companies such as Google, PayPal and Meta
• Established ICT companies such as IBM, Microsoft and Intel
• ICT Start-ups
Graduates can also pursue a range of MSc or PhD programmes such as the MSc Computer Science (Negotiated Learning).
Students take 7 core modules and 1 15-credit option module. Students take a further 10 credits from elective modules or may take one option module from List B.
Students take 6 core modules (40 credits) and 4 Option modules (20 credits).
Module ID | Module Title | Trimester | Credits |
---|---|---|---|
COMP30030 | Introduction to Artificial Intelligence | Autumn | 5 |
COMP30760 | Data Science in Python - DS | Autumn | 5 |
COMP30940 | Information Security | Autumn | 5 |
STAT20200 | Probability | Autumn | 5 |
COMP30750 | Information Visualisation -DS | Spring | 5 |
COMP30770 | Programming for Big Data | Spring | 5 |
COMP30850 | Network Analysis | Spring | 5 |
Stage 3 Options - A)1OF: All students should select COMP30780 at the start of the academic year. Students who wish to apply for the Industry Internship module and are successfully placed on an internship will be manually registered by the School Office to COMP30790 and subsequently dropped from COMP30780. Further information is available at: http://www.ucd.ie/science/careers/internships/students/ |
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COMP30790 | Industry internship | 2 Trimester duration (Spr-Sum) | 15 |
COMP30780 | Data Science in Practice | Spring | 15 |
Stage 3 Options - A)1OF: All students should select COMP30780 at the start of the academic year. Students who wish to apply for the Industry Internship module and are successfully placed on an internship will be manually registered by the School Office to COMP30790 and subsequently dropped from COMP30780. Further information is available at: http://www.ucd.ie/science/careers/internships/students/ |
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COMP30010 | Foundations of Computing | Autumn | 5 |
COMP30230 | Connectionist Computing | Autumn | 5 |
COMP30250 | Parallel Computing | Autumn | 5 |
COMP30170 | Computer Science Project | 2 Trimester duration (Aut-Spr) | 15 |
COMP30520 | Cloud Computing (UG) | Autumn | 5 |
COMP40370 | Data Mining | Autumn | 5 |
COMP47490 | Machine Learning (UG) | Autumn | 5 |
COMP30930 | Optimisation | Spring | 5 |
COMP47580 | Recommender Systems & Collective Intelligence | Spring | 5 |
COMP30230 | Connectionist Computing | Autumn | 5 |
COMP30250 | Parallel Computing | Autumn | 5 |
COMP30690 | Information Theory | Autumn | 5 |
COMP41400 | Multi-Agent Systems | Autumn | 5 |
SCI30080 | Professional Placement-Science | Autumn | 5 |
COMP30110 | Spatial Information Systems | Spring | 5 |
COMP30220 | Distributed Systems | Spring | 5 |
COMP30540 | Game Development | Spring | 5 |
COMP40020 | Human Language Technologies | Spring | 5 |
COMP40660 | Advances in Wireless Networking | Spring | 5 |
COMP41710 | Human Computer Interaction | Spring | 5 |
COMP47480 | Contemporary Software Development | Spring | 5 |
COMP47590 | Advanced Machine Learning | Spring | 5 |
COMP47650 | Deep Learning | Spring | 5 |
COMP47700 | Speech and Audio | Spring | 5 |
COMP47980 | Generative AI: Language Models | Spring | 5 |
IS30370 | Digital Media Ethics (formerly Information Ethics) | Spring | 5 |
MATH30180 | An Intro to Coding Theory | Spring | 5 |
STAT30280 | Inference for Data Analytics (online) | Spring | 5 |
Award | GPA | ||||
---|---|---|---|---|---|
Programme | Module Weightings | Rule Description | Description | ||
BHSCI014 | Stage 4 - 70.00% Stage 3 - 30.00% |
Standard Honours Award | First Class Honours | 3.68 |
4.20 |
Second Class Honours, Grade 1 | 3.08 |
3.67 |
|||
Second Class Honours, Grade 2 | 2.48 |
3.07 |
|||
Pass | 2.00 |
2.47 |
|||
BHSCI014 | Stage 4 - 70.00% Stage 3 - 30.00% |
Standard Honours Award | First Class Honours | 3.68 |
4.20 |
Second Class Honours, Grade 1 | 3.08 |
3.67 |
|||
Second Class Honours, Grade 2 | 2.48 |
3.07 |
|||
Pass | 2.00 |
2.47 |