Data & Computational Science

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.



For queries, please email us at dataandcomp@ucd.ie



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 handson experience in computational science and will allow you to apply the key theoretical and practical skills by working on a challenging research topic.



Who should apply?

Full Time option suitable for:



Domestic(EEA) applicants: Yes

International (Non EEA) applicants currently residing outside of the EEA Region. Yes



The MSc in Data and Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science.



Course Description

Computational science is at the crossroads between modern applied mathematics and statistics, and our programme recognizes this fact by combining aspects of both in a unique set of tailored modules including scientific computing, mathematical modelling, and data analytics.

Core Modules in Computational Science and Mathematics



Optimisation in Machine Learning

Applied Matrix Theory

Uncertainty Quantification

Data Programming with Python

Data Programming with R

Core Modules in Statistics and Data Analytics



Probability and Statistics

Predictive Analytics

Multivariate Analysis

Statistical Machine Learning

Bayesian Analysis

Optional Modules Include



Machine Learning and AI

Scientific Computing

High-performance Computing

Mathematica for Research

Numerical Algorithms

Time Series Analysis

Monte Carlo Inference

Modules and topics shown are subject to change and are not guaranteed by UCD

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

How to apply?

The following entry routes are available:



MSc Data & Computational Science FT (T306)

Duration 1 Years

Attend Full Time

Deadline Rolling*



* Courses will remain open until such time as all places have been filled, therefore early application is advised



Apply online at www.ucd.ie/apply and find the programme using the unique programme identifier: T306



For queries, please email dataandcomp@ucd.ie

1 year full-time.

Next Intake: 2024/2025 September.

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 past graduates from this programme and other similar past programmes in the School work in firms including, ICT companies (e.g. Amazon, Meta, Geowox, Sage, Version 1, Vodafone), the financial services industry (e.g. Allianz, Aon, Deloitte, Fidelity Investments, KPMG, Permanent TSB) and other data-intensive businesses (e.g. Accenture, IBM, Intel).