Data & Computational Science

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 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 the key theoretical and practical skills by working on a challenging research topic.



What Will I Learn?

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

Stage 1 Core Modules

MATH40550 Applied Matrix Theory Autumn 5

STAT20230 Modern Regression Analysis Autumn 5

STAT30340 Data Programming with R Autumn 5

STAT40800 Data Prog with Python (online) Autumn 5

STAT41040 Principles of Prob & Stats Autumn 5

ACM40990 Optimisation in ML Spring 5

ACM41000 Uncertainty Quantification Spring 5

STAT40150 Multivariate Analysis Spring 5

STAT40850 Bayesian Analysis (online) Spring 5



Stage 1 Options - A)3 of:

Students must take 15 credits

ACM40290 Numerical Algorithms Autumn 5

ACM40660 Scientific Programming Concepts (ICHEC) Autumn 5

STAT40400 Monte Carlo Inference Autumn 5

ACM40640 High Performance Computing (ICHEC) Spring 5

STAT30250 Advanced Predictive Analytics Spring 5

STAT30270 Statistical Machine Learning Spring 5

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

STAT40810 Stochastic Models (online) Summer 5

STAT40830 Adv Data Prog with R (online) Summer 5

STAT40840 Data Prog with SAS (online) Summer 5

STAT40950 Adv Bayesian Analysis (online) Summer 5

STAT40960 Stat Network Analysis (online) Summer 5

- 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.



These are the minimum entry requirements – additional criteria may be requested for some programmes



You may be eligible for Recognition of Prior Learning (RPL), as UCD recognises formal, informal, and/or experiential learning. RPL may be awarded to gain Admission and/or credit exemptions on a programme. Please visit the UCD Registry RPL web page for further information. Any exceptions are also listed on this webpage. https://tinyurl.com/2ae2ffax

1 Year Full Time

Next Intake: September 2025. Delivery: On Campus.

Career & Graduate Study Opportunities

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).