Postgraduate Certificate in Large Language Models
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Course Overview
The Postgraduate Certificate in Large Language Models is a 10-credit, level 9 conversion programme devised to provide up-skilling/re-skilling opportunities for numerate graduates. The programme is aimed at IT professionals with competent programming skills wishing to develop their skills in AI and Large Language models.
What makes this course different
Skills shortages in the area of data analytics has been highlighted by the Department of Enterprise, Trade and Employment, with "statisticians specialising in big data analytics with skills in IT, data mining, modelling, and advanced maths or related and relevant specialist skills" listed as a critical skill.
This course is ideal for numerate, technically capable graduates looking to enhance their skills and improve their employability in the field of Data Analytics.
Understanding the Industry
Large Language Models (LLMs) are truly disruptive technologies, poised to transform virtually every sector. For IT professionals, understanding these advancements is essential to staying relevant and competitive in a rapidly evolving landscape.
Career Opportunities
The demand for IT professionals with AI expertise is growing rapidly, as these technologies are set to reshape many existing roles. DKIT is committed to equipping participants with an accredited qualification that employers can trust as a credible and objective measure of knowledge in this fast-evolving field.
In PluralSite’s 2025 Skills Report, they find: “50% of companies give strong preference to job candidates with AI skills”.
Course Delivery and Modules
This online course will be delivered over 2 semesters, (Jan26 – May 26; Sept 26-Jan 27). Weekly classes will be delivered in a live synchronous format, 1 evening per week, typically Mondays or Wednesdays from 6pm to 8:30pm.
Attendance and participation in live classes will be important in order to master the material. Live online classes may be supported by additional asynchronous activity (videos/ resources and tasks) which participants complete in their own time within the same week. Modules will be delivered in Python. Good Python programming skills will be required to access material and achieve the Learning Outcomes.
- Module 1: Introduction to Large Language Models
- Module 2: Retrieval-Augmented Generation (RAG) & Information Retrieval
Fees and Funding
This programme is funded through the South East Cross Border Alliance (SECBA) project, supported by PEACEPLUS, a programme managed by the Special EU Programmes Body (SEUPB).
Fees
Entry requirements
This standard minimum entry requirements is a 2.2 Honours Degree (NFQ Level 8) or equivalent in STEM (Science, Technology, Engineering, and Mathematics), who have substantial technical competencies and strong programming skills. A good grounding in Maths will be required to access material related to Machine Learning.
Recognition of Prior Learning
Applicants who do not meet the standard academic entry requirements but have significant relevant experience (certified and/or experiential) may apply to access this programme via the Recognition of Prior Learning (RPL) route. Learn more about RPL at DkIT
How to apply
Applications will open soon. Register your interest below and we’ll notify you once they open.
Ask us a Question
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Disclaimer: All module titles are subject to change and for indicative purposes only. All courses are delivered subject to demand and timetables are subject to change. Elective Module options will only run subject to student numbers. The relevant Department will determine the viability of each elective module option proceeding depending on the number of students who choose that option. Students will be offered alternative elective modules on their programme should their preferred elective option not be proceeding. Award Options for Common Entry Programmes: The relevant Department will determine the viability of each award option proceeding depending on the number of students who choose either option. If the numbers for one of the Award options exceed available places, students for this option will be selected based on Academic Merit (highest grades).