MA Statistics

Graduate Taught (level 9 nfq, credits 120)

The MA in statistics will bring students to the same level as the 1 year MSc degree in Statistics

Currently, students without sufficient statistical background knowledge can attain masters level proficiency by first completing the Higher Diploma in Statistics followed by the MSc in Statistics, which takes two years. The MA in Statistics provides an alternative pathway in 16 months and there is no comparable programme in Ireland or the UK.

Ranked in the top 200 in the world in the QS World University Ranking, and is the only statistics group in Ireland to appear in this ranking.
The UCD MA Statistics is fully accredited by the Royal Statistical Society
Exposure to cutting-edge modern statistical applications

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Who should apply?

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

This programme is intended for students with a numerate background but who may have insufficient background knowledge to gain entry to the MSc programme.

Career Opportunities

Career prospects on completion of the MA in Statistics are equivalent to that of the MSc in Statistics and graduates pursue careers in the pharmaceutical industry (e.g. Elan, Quintiles), banking, finance, and risk management.

There is increased demand for statisticians from the IT sector (e.g. Google, Intel, data mining companies). In addition many government departments employ statisticians. Some past students embarked on a career in academia by proceeding to study for a PhD in Statistics. Former MSc and MA students are currently working for such firms as Google, Western Union, AIB, Norbrook, EY, O2 and SPSS, while others chose to do a PhD. Demand for graduates continues to be strong both in Ireland and abroad.

What modules can I take?

View All Modules Here

There are 120 credits of work to do spread over four semesters. The first two semesters involve taking nine 5-credit modules (45 credits) from the Higher Diploma programme, the module MEEN 40670 Technical Communication(5 credits) together with two modules at 7.5 credit (15 credits) from the M.Sc. programme. The third semester involves a 25 credit research or data analytic project. The fourth semester involves taking four modules at 7.5 credits (30 credits) at level 4 from the M.Sc. programme.

Students must take all Core modules in addition to the dissertation in order to complete the programme.

Fees, Funding and Scholarships

MA Statistics (F043) Full Time
EU fee per year - € 9790
nonEU fee per year - € 18300

***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.

Entry Requirements

This programme is intended for applicants with a degree in Mathematics, Economics, Finance, certain Engineering degrees or similar quantitative disciplines where statistics has formed some component of the degree. An upper second class honours, or international equivalent is required.
Applicants who do not meet these requirements but can demonstrate an interest and ability in statistics may be considered.
Alternatively students may qualify for enrolment to the Higher Diploma Statistics from which they can gain entry to the 1-year MSc in Statistics.
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

Testimonial

Staff Profile
Dr Andrew Parnell,
UCD School of Mathematical Sciences,
Complex and Adaptive Systems Laboratory

My main statistical interest is in Bayesian stochastic processes and computational statistics. I am particularly interested in Gaussian processes, stochastic volatility models, compositional data analysis and monotonic processes. I have applied such models to a variety of environmental, biomedical and ecological science problems relating to: spatiotemporal climate reconstruction, stable isotope mixing models, the development of biomarker panels for prostate cancer, sea-level change and radiocarbon dating.