Master of Science (MSc) in Data Analytics.
With increased availability of information about customer preferences and actions, production processes and supply chains, there is growing recognition of the economic returns from the use of big data and analytics. The Government's Action Plan for Jobs 2013 sets out seven 'disruptive reforms' being prioritised with major potential to have a significant impact on job creation, to support enterprises or where Ireland can profit from a natural advantage or opportunity that presents itself in the economy.
One of these goals is to make Ireland a leading country in Europe for big data. This programme accommodates a wide audience of learners whose specific interests in data analytics may be either technically focused or business focused.
Either full time or part time, the programme is designed to facilitate learners with a statistics/computing/technology background who wish to upskill in this new and emerging area of Data Analytics. It will also be of interest to learners who have completed their undergraduate degree and wish to specialise in this area. It may be their preference to take the theory and practical modules only and not complete the dissertation.
Aims and Objectives
This is an innovative programme with an integrated delivery from end-to-end covering a wide range of data analytics topics. The programme aims to develop learners' knowledge of the theory and practice of Data Analytics necessary for them to secure employment and perform at postgraduate level in the areas of ICT /Data Analytics in a broad range of commercial, industrial and public sector environments. Graduates will have an in-depth knowledge of the practical and theoretical aspects of data analytics. The programme enables and supports learners in developing critical analytical skills and in applying theoretical concepts to the practice of data analytics.
The programme incorporates Personal & Professional Development (PPD) within modules to enhance the student's employability, which will enable them to integrate seamlessly into an organisation by addressing skills such as leadership, self-management and teamwork that are essential in the area of Data Analytics. It also comprises a Research Methods module integrated into the applied data analytics modules, focusing on literature review, research technique, and their application to real life situations, data sets and how applied research and entrepreneurship are linked. These modules will inform the learner's Dissertation, which requires the production of an artefact.
The specific programme aims are as follows:
• To enable learners to develop expert knowledge and analytical skills in current and developing areas of analysis statistics, and machine learning.
•To provide learners with a deep and systematic knowledge of business and technical strategies for data analytics and the subsequent skills to implement solutions in these areas.
• To facilitate the development by the learner of applied skills that are directly complementary and relevant to the workplace.
• To identify and develop autonomous learning skills for the learner.
• To develop in the learner a deep and systematic understanding of current issues of research and analysis
•To enable the learner to identify, develop and apply detailed analytical, creative, problem solving and research skills.
• Provide the learner with a comprehensive platform for career development, innovation and further study.
The programme is structured in two sequential stages. Stage 1 is a taught component, contributing 60 credits. Stage 2 is a supervised dissertation, contributing 30 credits. The dissertation provides students with the opportunity to critically review the literature in any part of the taught syllabus.
For full-time students, the taught component consists of six modules. Successful completion of the taught component stage allows you to move on to the dissertation stage.
For part-time students, delivery of the programme is structured over four taught semesters. During each semester, students are typically required to attend lectures on two evenings per week and occasional weekend workshops
The course has six key components:
1. Class room lectures
2. Case Based learning
3. Practical Skills Sessions
6. Individual and Group work