MSc Data & Computational Science

Graduate Taught (level 9 nfq, credits 90)

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.

Download the course brochure (pdf)

  • 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.

Careers & Employability

The unique combination of modules and skills offered by this programme will equip graduates to work in a range of specific sectors in data analytics, data science, quantitative modelling in finance, and computational science and engineering. This is a new highly specialized programme in the school; recent past graduates from similar programmes in the school work in firms including:

  • ICT companies (e.g. Google, Paddy Power, LinkedIn)
  • The financial services industry (e.g. Citi, Deloitte, Geneva Trading, Murex)

Full Time option suitable for:

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

Part Time option suitable for:

Domestic(EEA) applicants: Yes
International (Non EEA) applicants currently residing outside of the EEA Region. No

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.

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.

This programme is aimed at students who wish to gain a deep understanding of applied mathematics, statistics and computational science at the graduate level. The programme 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 industry or in academia.The taught modules in the programme provide a thorough grounding in the areas of applied mathematics, statistics and computational science; the (supervised) research project introduces the students to an area of computational research.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. We expect our students to become autonomous learners and researchers capable of setting their own research agenda. 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.We value students who already have a strong numerate training and are motivated to take further their knowledge in this area. We aim to provide a teaching and learning environment that develops confidence and independence through a wide variety of interactive formats, both inside and outside the classroom.

 

View All Modules Here

Course content

 

Core modules in simulation and modelling:

  • Simulation Modelling and Analysis
  • C and Fortran programming
  • Parallel computing using MPI
  • Mathematica for Research

 

Core modules in statistics and data analytics

  • Linear Models
  • Statistical Data Mining
  • Data programming
  • Multivariate Analysis
  • Bayesian Analysis

 

Optional topical modules, for example:

  • Cryptography and Coding Theory
  • Advanced Fluid Mechanics
  • Weather and Climate
  • Financial Mathematics

 

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

MSc Data & Computational Science (T306) Full Time
EU          fee per year - € 7760
nonEU    fee per year - € 19200

MSc Data & Computational Science (T307) Part Time
EU          fee per year - € 4235
nonEU    fee per year - € 12400

***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 see 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

Staff Profile

Dr Conor Sweeney

School of Mathematics and Statistics

I am a lecturer in Applied and Computational Mathematics and am also a member of the Meteorology and Climate Centre and the UCD Earth Institute.  In my research, I am interested in making our weather forecasts better, and improving our understanding of climate change. To do this, I run mathematical models of the atmosphere and the Earth System at High Performance Computing centres such as the Irish Centre for High-End Computing and the European Centre for Medium-Range Weather Forecasts. No model is perfect, however, so my research also involves developing new statistical techniques to remove any systematic errors in the forecasts, and to quantify uncertainties in the model output.

Current research applications include:

·         improved forecasting methods for renewable energy and Energy Systems Integration

·         extreme events in climate data

·         tracking and analysing Ireland's storms from 1900-2100

There are always new challenges in modelling the weather and climate, and the demand for improved methods and skills is increasing all the time!

The following entry routes are available:

MSc Data & Computational Science FT (T306)
Duration
1 Years
Attendance
Full Time
Deadline
Rolling *
MSc Data & Computational Science PT (T307)
Duration
2 Years
Attendance
Part 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