Course Summary
The programme aims to develop highly skilled and competent graduates in the rapidly expanding field of Data Science. The programme has been designed and developed with industry experts to ensure that graduates develop core skills in programming, database management, statistical modelling, time series analysis, machine learning, data visualisation and interpretation.
College Link
Career Sectors
This course prepares you for working in the Career Sectors below. Follow the links to get a fuller understanding of the sectors you are preparing for.
Entry Requirements
Applicants will already hold a primary degree, and must be highly motivated, interested in data science and capable of independent learning. Preference will be given to applicants with a background in cognate and analytical disciplines, who would benefit from an opportunity to gain expertise in ICT (data science) skills which are particularly relevant to industry.
All candidates with a Level 8 qualification or equivalent will be considered. Candidates with a Level 7 qualification and significant relevant experiential learning may be eligible through our recognition of prior learning processes.
What is RPL?
Recognition of Prior Learning (RPL) is when formal recognition is given for what you already know prior to starting on a programme or module. With recognition of prior learning the focus is on learning and not on experience as such. You can apply for RPL in any MTU accredited programme or module. Programmes which are accredited by professional bodies or any external awarding bodies may have their own procedures for RPL which you should refer to.
Application Details
Fees
€6300 under the Springboard initiative.
Non-Springboard applications please contact [email protected]
The Student
Career Interests
This course is typically suited for people with the following Career Interests. If these interests do not describe you, this course may prepare you for work you may not find satisfying.
Investigative
The Investigative person will usually find a particular area of science to be of interest. They are inclined toward intellectual and analytical activities and enjoy observation and theory. They may prefer thought to action, and enjoy the challenge of solving problems with sophiscticated technology. These types prefer mentally stimulating environments and often pay close attention to developments in their chosen field.