1 - Demonstrate an in-depth understanding of the interface of applied mathematics, statistics and computational science.
2 - Demonstrate familiarity with the areas of data and computational science currently under active research
3 - Undertake excellent research at an appropriate level, including survey and synthesise the known literature
4 - Use the language of logic to reason correctly and make deductions
5 - Approach problems in an analytical, precise and rigorous way
6 - Apply computationally based techniques to formulate and solve problems
7 - Model real-world problems in an applied mathematical or statistical framework
8 - Analyse and interpret data, find patterns and draw conclusions
9 - Work independently and be able to pursue a research agenda
10 - Give oral presentations of technical material at a level appropriate for the audience
11 - Prepare a written report on technical content in clear and precise language
This course is available through the following application route(s)
The MSc Data & Computational Science course is aimed at students who wish to gain a deep understanding of applied mathematics, statistics and computational science at the graduate level. The course will equip such students with the skills necessary to carry out research in these computationally based sciences and will prepare them well for a career either in the industry or in academia. The taught modules in the course provide a thorough grounding in the areas of applied mathematics, statistics and computational science; all students complete project work in data and computational science with the option of (supervised) research dissertation.
We expect our students to gain a thorough understanding of data and computational science at the graduate level, as well as a broad understanding of currently relevant areas of active research and to become autonomous learners and researchers capable of setting their own research agenda.
- The programme will equip you to solve complex scientific problems and analyse large data sets using a range of theoretical tools, from deterministic mathematical modelling to Bayesian analysis.
- The intensive programming modules will allow you to develop a range of sought-after skills in practical programming and data analytics, including applications in high-performance computing.
- Topical application areas are offered each year, including cryptography, numerical weather prediction, and financial mathematics. The dissertation will give you further hands-on experience in computational science and will allow you to apply key theoretical and practical skills by working on a challenging research topic.
About This Course
Our graduates will be suitably qualified for research at the PhD level at the interface of applied mathematics, statistics and computational science. They will be valued for their technical knowledge and research skills. Equally, our graduates will be in demand by employers for their acquired skills in data analytics and computational and statistical modelling. Recent graduates from this programme work in ICT (including Amazon, IBM, Intel, Meta, Paypal and Vodafone), financial services (including AIB, Aon, Fidelity Investments), and other data-intensive industries (e.g. Accenture, Bosch, EY)
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 1 Core Modules |
MATH40550 | Applied Matrix Theory | Autumn | 5 |
Stage 1 Core Modules |
STAT20230 | Modern Regression Analysis | Autumn | 5 |
Stage 1 Core Modules |
STAT30340 | Data Programming with R | Autumn | 5 |
Stage 1 Core Modules |
STAT40800 | Data Prog with Python (online) | Autumn | 5 |
Stage 1 Core Modules |
STAT41040 | Principles of Prob & Stats | Autumn | 5 |
Stage 1 Core Modules |
ACM40990 | Optimisation in ML | Spring | 5 |
Stage 1 Core Modules |
ACM41000 | Uncertainty Quantification | Spring | 5 |
Stage 1 Core Modules |
STAT40150 | Multivariate Analysis | Spring | 5 |
Stage 1 Core Modules |
STAT40850 | Bayesian Analysis (online) | Spring | 5 |
Stage 1 Options - A)3 of: Students must take 15 credits |
ACM40290 | Numerical Algorithms | Autumn | 5 |
Stage 1 Options - A)3 of: Students must take 15 credits |
ACM40660 | Scientific Programming Concepts (ICHEC) | Autumn | 5 |
Stage 1 Options - A)3 of: Students must take 15 credits |
STAT40400 | Monte Carlo Inference | Autumn | 5 |
Stage 1 Options - A)3 of: Students must take 15 credits |
ACM40640 | High Performance Computing (ICHEC) | Spring | 5 |
Stage 1 Options - A)3 of: Students must take 15 credits |
STAT30250 | Advanced Predictive Analytics | Spring | 5 |
Stage 1 Options - A)3 of: Students must take 15 credits |
STAT30270 | Statistical Machine Learning | Spring | 5 |
Stage 1 Options - A)3 of: Students must take 15 credits |
STAT40970 | Machine Learning & AI (online) | Spring | 5 |
Stage 1 Options - B)1 of: Students complete a dissertation under academic supervision |
ACM40910 | Research Project II | Summer | 30 |
Stage 1 Options - C)1 of: Students on Stream 2 must complete this core module |
ACM40960 | Projects in Maths Modelling | Summer | 15 |
Stage 1 Options - D)3 of: Students on Stream 2 must take 15 credits from this option list |
STAT40780 | Data Prog with C (online) | Summer | 5 |
Stage 1 Options - D)3 of: Students on Stream 2 must take 15 credits from this option list |
STAT40810 | Stochastic Models (online) | Summer | 5 |
Stage 1 Options - D)3 of: Students on Stream 2 must take 15 credits from this option list |
STAT40830 | Adv Data Prog with R (online) | Summer | 5 |
Stage 1 Options - D)3 of: Students on Stream 2 must take 15 credits from this option list |
STAT40840 | Data Prog with SAS (online) | Summer | 5 |
Stage 1 Options - D)3 of: Students on Stream 2 must take 15 credits from this option list |
STAT40950 | Adv Bayesian Analysis (online) | Summer | 5 |
Stage 1 Options - D)3 of: Students on Stream 2 must take 15 credits from this option list |
STAT40960 | Stat Network Analysis (online) | Summer | 5 |
Cian O’Callaghan, Paddy Power
I would thoroughly recommend the MSc Data & Computational Science to students interested in pursuing a career/further studies in data science. The lecturers and tutors are both extremely knowledgeable and approachable. The course strikes a balance between understanding the theory behind computational and machine learning algorithms and applying this theory to real-world problems.
EU fee per year - € 9530
nonEU fee per year - € 26660
***Fees are subject to change
Tuition fee information is available on the UCD Fees website. Please note that UCD offers a number of graduate scholarships for full-time, self-funding international students, holding an offer of a place on a UCD graduate degree programme. For further information please visit International Scholarships.
- This programme is intended for applicants who have an Upper Second class honours degree or higher, or the international equivalent, in a highly quantitative subject such as Mathematics, Physics, Statistics, Engineering.
- Applicants whose first language is not English must also demonstrate English language proficiency of IELTS 6.5 (no band less than 6.0 in each element), or equivalent.
School of Mathematics and Statistics Application Process FAQ
These are the minimum entry requirements – additional criteria may be requested for some programmes
Full Time option suitable for:
Domestic(EEA) applicants: Yes
International (Non EU) applicants: Yes
The MSc in Data & Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science.
How to Apply
General application route(s) for Irish/UK/EU applicants* for International (non-EU) applicants* to Data & Computational Science:
| ROWCLASS | Apply to | Application Type | ||
|---|---|---|---|---|
| showAudience-audienceEU showAudience-audienceInt | T306 Data & Computational Science Master of Science |
Full-Time Commencing September 2024 |
Graduate Taught | Closed |
| showAudience-audienceEU showAudience-audienceInt | T306 Data & Computational Science Master of Science |
Full-Time Commencing September 2025 |
Graduate Taught | Apply |