This one year full-time conversion course is intended for students who wish to deepen their statistical skills and develop an in-depth and applied understanding of sustainability from a variety of perspectives.
This is a flagship programme delivered through Trinity’s E3 ‘Engineering, Environment and Emerging Technology’ initiative and the course embodies the E3 mission to create a more sustainable future for our world. The course is designed to cater to students from diverse backgrounds through flexible learning options in the course modules.
The course provides students with a wide range of modelling, computing, and statistical skills, and they will study a variety of sustainability topics that are key to developing solutions to environmental challenges from global to local levels. Communication skills will be developed via individual and team project work embedded in the curriculum, and there is an emphasis on developing student’s skills to translate statistical outcomes to broad audiences in multiple contexts.
Is This Course For Me?
Anyone interested in understanding the natural environment, its resources, and how to best use them in a sustainable manner should study Statistics and Sustainability. This could include students studying environmental science, natural resource management, economics, public policy, and business. As the world becomes increasingly aware of the need to use resources responsibly, more people are turning to Statistics to help them understand how to best accomplish this.
Designed for graduates of disciplines other than statistics who want to develop and deepen their knowledge of statistical methods for solving problems involving data, this course allows graduates to increase their employability and apply the ideas and methods to which they have been introduced in their own work. Graduates from the M.Sc. in Statistics and Sustainability will emerge highly numerate and statistically skilled, and with an in-depth understanding of the sustainability and environmental issues facing today’s world.
The full programme, comprising 90 ECTS credits, takes one calendar year to complete and leads to the qualification of M.Sc. in Statistics and Sustainability, a Level 9 award under the Irish National Framework for Qualifications (NFQ) Level-9.
The course runs full-time over a twelve-month period commencing late September and completing early September the following year. Formal teaching is divided into two twelve-week semesters, running from September-December and from January-April. By April, students will have chosen their dissertation topic, in consultation with their chosen supervisor, and be ready to develop substantial time researching and prototyping their work.
Students will study sustainability topics key to developing solutions to environmental challenges. They will also gain experience in developing and applying statistical models, and geographic information systems and remote sensing technologies, to local and global sustainability problems. They will be exposed to the underlying theory of the statistical methodologies studied to deepen their statistical skills and their ability to promote statistical innovation.
Students will take modules that include: Foundations of Statistics; Advanced Linear Models I; Advanced Linear Models II; GIS: Geographic Information Systems; The Sustainable Green Organisation; Research Methods; Statistics and Sustainability Group Projects; and a final written Dissertation.
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Study Statistics and Sustainability (M.Sc. / P.Grad.Dip.) at Trinity
An introduction to the M.Sc. in Statistics and Sustainability by Caroline Brophy of the School of Computer Science and Statistics, Trinity College Dublin.
AwardsNFQ Level 9
Number of Places9 EU and 16 non-EU Places
Professor Arthur White
31st July 2024
Applicants must have first-class or upper second-class (2.1) Honours undergraduate degree that includes at least one year of university-level Mathematics (including linear algebra and calculus topics). The programme also caters for applicants who already have an undergraduate degree in Mathematics, Statistics, or other highly quantitative subjects.
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