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Masters Degrees in Computational Mathematics, Ireland

We have 5 Masters Degrees in Computational Mathematics, Ireland

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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. Read more
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.

- 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.

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The Master of Science in Quantitative Finance attracts numerate, competitive students seeking careers in finance that stretch their quantitative talents. Read more
The Master of Science in Quantitative Finance attracts numerate, competitive students seeking careers in finance that stretch their quantitative talents. The two-year full-time programme provides an integrated and balanced curriculum of finance and computational methods that accommodates students of diverse backgrounds.

The 4-semester programme comprises 18 courses, an internship and a major project. The curriculum gives students the knowledge and skills to develop quantitative security pricing models and trading strategies for managing portfolios of equity, fixed income and derivative securities and for managing corporate financial risk.

Programme structure
The MScQF is a small programme limited to about 20 students per year. The design of the curriculum presumes relatively little prior training in either finance or mathematics. The two-year full-time MScQF integrates the content of the one-year MBS in Finance with nearly the
equivalent of a masters in applied financial mathematics. The two-year structure affords the opportunity for a summer internship with a financial firm, a crucial component of the entire training and job-hunting process.
Assessment is by both examination and project work.

Careers
Career options for MScQF graduates include funds management, investment banking, financial engineering, and corporate treasury management. The MScQF also provides the recommended coursework component of the PhD in Finance at
UCD, leading to careers in academia as well as the finance industry.

Entry requirements
Appropriate degree fields include mathematical finance, economics, mathematics, statistics, engineering and physics. As relatively little prior formal training in mathematics is presumed, we welcome applications from quantitatively inclined graduates from a standard commerce programme. Generally, applicants must have high grades and a high score on the mathematics component of the GMAT (or an equivalent test). Work experience can partially compensate for non-competitive grades.

Ask us about our sponsorship and internship opportunities!

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The MSc in Computational Finance is an intensive 12 month, full-time programme which combines theoretical rigour with a practical emphasis on the acquisition of advanced computational finance skills. Read more
The MSc in Computational Finance is an intensive 12 month, full-time programme which combines theoretical rigour with a practical emphasis on the acquisition of advanced computational finance skills. It is this blend of advanced analytical and quantitative skills, combined with the innovative use of experiential-learning techniques such as simulated 'live' financial markets trading workshops, which will differentiate graduates of this programme from other programmes in the field.

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This course will help you analyse and understand the large data sets that are regularly being created via the huge growth in freely available online information. Read more
This course will help you analyse and understand the large data sets that are regularly being created via the huge growth in freely available online information. This is an exceedingly valuable skill and in strong demand from employers. We see Data Analytics as a subject at the crossroads between statistics and computer science, and our Online Professional Diploma contains elements of both. We will give you the tools to apply these advanced skills to maximum effect in any work-related environment.

There are no lectures to attend. Students will be given videos, online demonstrations, and interactive games to enhance their learning, with regular feedback and interaction via course tutors through the UCD website.

Please visit our School website for more information.

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The Online MSc in Data Analytics covers 18 5-credit modules, two per semester over 9 semesters or 3 years, of which the Online Professional Diploma in Data Analytics covers the first 4. Read more
The Online MSc in Data Analytics covers 18 5-credit modules, two per semester over 9 semesters or 3 years, of which the Online Professional Diploma in Data Analytics covers the first 4. This first year is designed to introduce you to statistical and mathematical concepts in Data Analytics and Data Mining, and to get you started on programming with data. The second year is split between understanding the theory behind statistical and mathematical models for data via predictive analytics, and dealing with data sets at scale using Python and multivariate techniques. The final year covers some advanced methods: Monte Carlo, Bayesian Analysis, Time Series Data, and Complex Stochastic models. A provisional list of topics is as follows:

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