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.

1 - Demonstrate understanding of specific bodies of knowledge within the disciplines of Computer Science and Data Science relating to the manipulation and preprocessing of large volumes of data and the statistical analysis of data.
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.

Students who return failing grades in a trimester amounting to 15 credits, or more, will be identified under the UCD Continuation – Academic Progress policy. Students whose rate of progression and performance over two academic years is deemed unacceptable will be referred to the Governing Board to be reviewed for exclusion from the programme.

Students who fail to progress from the stage of the programme they are registered to for more than two academic years (except where a period of Leave of Absence has been granted for one of those years) will also be contacted under the Continuation – Academic Progress Policy.

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.

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 Facebook


• 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) or the MSc Digital Forensics and Cybercrime Investigation.

Students take 7 core modules and 1 15-credit option module. Students take a further 10 credits as electives.

Students take 7 core modules (40 credits) and 4 Option modules (20 credits).

Module ID	Module Title	Trimester	Credits
Stage 3 Core Modules
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 Core Modules
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/
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/
Stage 4 Core Modules
COMP30520	Cloud Computing (UG)	Autumn	5
COMP30900	Final Year Project Foundations	Autumn	5
COMP40370	Data Mining	Autumn	5
COMP47490	Machine Learning (UG)	Autumn	5
COMP30910	FYP: Design and Implementation	Spring	10
COMP30930	Optimisation	Spring	5
COMP47580	Recommender Systems & Collective Intelligence	Spring	5
Stage 4 Core Modules
Stage 4 Options - A)MIN4OF: Students must select 4 option modules from the list below.
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
COMP47480	Contemporary Software Development	Spring	5
COMP47650	Deep Learning	Spring	5
IS30370	Digital Media Ethics (formerly Information Ethics)	Spring	5
MATH30250	Cryptography: Theory & Practice	Spring	5
STAT30280	Inference for Data Analytics (online)	Spring	5
Stage 4 Options - A)MIN4OF: Students must select 4 option modules from the list below.

		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