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.
- School
- School of Computer Science
- Attendance
- Full Time
- Level
- Undergraduate
- NFQ Level
- 8
- Award
- Bachelor of Science
- Mode of Delivery
- On Campus
- Programme Director
- Assoc Professor Mel Ó Cinnéide
- Overall Programme Credits:
- 240
- Programme Credits:
- Stage 3
Core/Option: 50 Electives: 10
Stage 4
Core/Option: 60 Electives: 0 - Major/Minor Core & Option Credits:
- Stage 3: 50
Stage 4: 60
Curricular information is subject to change.
If you have an interest in technology and trends, this degree subject could be for you. At its core, data science is about extracting insights from data that can transform the way a company operates. For example, understanding data can match millions of businesses with new customers around the world in the areas of advertising and e-commerce. Mining large-scale data sets based on our health can inform pharmaceutical companies when choosing new medicines to develop and capturing data streams from wearable devices can improve our understanding of our habits and routines. Agri-food, energy, transport, government and education are all examples of industries on the verge of being transformed by the power of data-driven methods.
2 - Develop new insights from the analysis of data.
3 - Have a broad awareness of related bodies of knowledge in Computer Science outside data analytics for example, computer networks, information security and software engineering.
4 - Make use of the insights and findings of research in Computer Science to inform their understanding of the field and how they operate within it.
5 - Work with established statistical and engineering methods in analysing data.
6 - Present their work in a public forum and communicate it to technical and non-technical audiences.
7 - Work as an individual and as a team member.
8 - Learn to work at varying scales and with projects of increasing complexity.
9 - Apply lessons learned in lectures, tutorials and practicals to develop the practice of "learning by doing", made evident in their assignments and project work and problem-solving strategies.
10 - Implement computer programs in a variety of programming languages and analyse and reason about these programs.
11 - Demonstrate awareness of issues (technical, financial, societal and ethical) in the areas of Computer Science and Data Analytics.
Where the rate of progression and performance over two academic years is deemed unacceptable, a case will be submitted to the Governing Board for review. A recommendation for discontinuation may be the outcome of this review.
As Stages 3 and 4 have the most dynamic components of the programme, and the material studied previously may no longer be relevant, a student who has been away from the programme for a significant period should be required to register again to Stage 3. The upper limit for completion of Stages 3 and 4 should be six years if they choose to do 120 credits with 20 in each year.
It is possible to study abroad for a trimester, usually in the third year of the course. Universities that students have visited to date include the University of Auckland, New Zealand, the University of California, Irvine, USA, and Fudan University, Shanghai, China.
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).
Associate Professor Mel Ó Cinnéide
UCD School of Computer Science
askscience@ucd.ie
Stage 3
Students take seven core modules, and one 15-credit option module from List A. Student must take a further 10 credits, selecting from option modules on List B, or elective modules.
Stage 4
Students take 6 core modules (40 credits) and 4 Option modules (20 credits).
Below is a list of all modules offered for this degree in the current academic year. Click on the module to discover what you will learn in the module, how you will learn and assessment feedback profile amongst other information.
Incoming Stage 1 undergraduates can usually select an Elective in the Spring Trimester. Most continuing undergraduate students can select up to two Elective modules (10 Credits) per stage. There is also the possibility to take up to 10 extra Elective credits.
| Trimester | Credits | |||
|---|---|---|---|---|
Stage 3 Core Modules |
COMP30030 | Introduction to Artificial Intelligence | Autumn | 5 |
Stage 3 Core Modules |
COMP30760 | Data Science in Python - DS | Autumn | 5 |
Stage 3 Core Modules |
COMP30940 | Information Security | Autumn | 5 |
Stage 3 Core Modules |
STAT20200 | Probability | Autumn | 5 |
Stage 3 Core Modules |
COMP30750 | Information Visualisation -DS | Spring | 5 |
Stage 3 Core Modules |
COMP30770 | Programming for Big Data | Spring | 5 |
Stage 3 Core Modules |
COMP30850 | Network Analysis | Spring | 5 |
Stage 3 Options - A)1 of: 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/ |
COMP30790 | Industry internship | 2 Trimester duration (Spr-Sum) | 15 |
Stage 3 Options - A)1 of: 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/ |
COMP30780 | Data Science in Practice | Spring | 15 |
Stage 3 Options - B) Min 0 of: Students may register to 10 elective credits or select additional option module(s) from the list below in order to fulfil their stage requirements. |
COMP30010 | Foundations of Computing | Autumn | 5 |
Stage 3 Options - B) Min 0 of: Students may register to 10 elective credits or select additional option module(s) from the list below in order to fulfil their stage requirements. |
COMP30230 | Connectionist Computing | Autumn | 5 |
Stage 3 Options - B) Min 0 of: Students may register to 10 elective credits or select additional option module(s) from the list below in order to fulfil their stage requirements. |
COMP30250 | Parallel Computing | Autumn | 5 |
Stage 3 Options - B) Min 0 of: Students may register to 10 elective credits or select additional option module(s) from the list below in order to fulfil their stage requirements. |
COMP30960 | Human Computer Interaction | Autumn | 5 |
Stage 4 Core Modules |
COMP30170 | Computer Science Project | 2 Trimester duration (Aut-Spr) | 15 |
Stage 4 Core Modules |
COMP30520 | Cloud Computing (UG) | Autumn | 5 |
Stage 4 Core Modules |
COMP40370 | Data Mining | Autumn | 5 |
Stage 4 Core Modules |
COMP47490 | Machine Learning (UG) | Autumn | 5 |
Stage 4 Core Modules |
COMP30930 | Optimisation | Spring | 5 |
Stage 4 Core Modules |
COMP47580 | Recommender Systems & Collective Intelligence | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP30230 | Connectionist Computing | Autumn | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP30250 | Parallel Computing | Autumn | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP30690 | Information Theory | Autumn | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP41400 | Multi-Agent Systems | Autumn | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP41740 | Human-Centred AI | Autumn | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
SCI30080 | Professional Placement-Science | Autumn | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP30110 | Spatial Information Systems | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP30220 | Distributed Systems | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP30540 | Game Development | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP40660 | Advances in Wireless Networking | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP47480 | Contemporary Software Development | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP47590 | Advanced Machine Learning | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP47650 | Deep Learning | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP47700 | Speech and Audio | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
COMP47980 | Generative AI: Language Models | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
IS30370 | Digital Media Ethics (formerly Information Ethics) | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
MATH30250 | Cryptography: Theory & Practice | Spring | 5 |
Stage 4 Options - A) Min 4 of: Students must select 4 option modules from the list below. |
STAT30280 | Inference for Data Analytics (online) | Spring | 5 |
Module Weighting Info
| 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 |
|||