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Accessibility & LanguageArtificial Intelligence for Professionals (Pgcert)
PgCert (Artificial Intelligence for Professionals)
College of Science and Engineering, School of Computer Science- Title of Award
- Postgraduate Certificate
- Course Code
- PGC-AIP
- Average Intake
- 20
- Delivery
- Online
- NFQ
- Level 9
- Award Type
- Major
- Next Intake
- September 2026
- Duration
- 1 year, part-time
- ECTS Weighting
- 30
Why Choose This Course?
Course Information
Who is this course for?
The PgCert in Artificial Intelligence for Professionals is targeted at learners who would benefit professionally from gaining a high-level understanding of the field of artificial intelligence (AI). This programme is suitable for those who currently work with AI, or for those who plan to work with AI in the future. The PgCert is also suitable for those who would like to explore the possibilities of how AI might be applied within their own organisation or industry sector.
What will I study?
The PgCert in Artificial Intelligence for Professionals is delivered 100% via remote learning, so students are not required to attend the University of Galway campus to complete the programme. The PgCert is delivered asynchronously, so students are free to learn at their own pace, at the times and places that best suit them. During each week of the programme, pre-recorded video lectures will be made available for students to watch via the university’s learning management system.
The pre-recorded video lectures will be supplemented by additional carefully curated content and activities, including assigned readings, discussion forums and other exercises. There will also be some optional synchronous live online sessions where students will have a chance to meet the lecturers who deliver the programme. The programme is assessed 100% via continuous assessment, so there is no need for students to attend University of Galway for traditional end-of-semester exams.
The programme is delivered on a part-time basis over 9 months and is designed so that it can be completed while students are in full-time employment. The PgCert is delivered over two teaching semesters (September - December and January - May), and in each semester students will take 3 modules. Each individual module is worth 5 ECTS, and in total the PgCert consists of 30 ECTS.
Semester 1 modules (September - December):
- CT5181 Introduction to AI (5 ECTS)
- CT5182 Machine Learning and Natural Language Processing (5 ECTS)
- CT5184 Data Analysis and Visualisation (5 ECTS)
Semester 2 modules (January - May):
- CT5212 Societal Impact of AI
- CT5185 Ethics and Law for AI (5 ECTS)
- CT5186 Future of AI (5 ECTS)
Curriculum Information
Curriculum information relates to the current academic year (in most cases).Course and module offerings and details may be subject to change.
Glossary of Terms
- Credits
- You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
- Module
- An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
- Optional
- A module you may choose to study.
- Required
- A module that you must study if you choose this course (or subject).
- Semester
- Most courses have 2 semesters (aka terms) per year.
Year 1 (30 Credits)
RequiredCT5181: Introduction to AI
CT5181: Introduction to AI
Semester 1 | Credits: 5
This module is intended as a broad introduction to the topic of Artificial Intelligence. Some key historical milestones and context will be provided as to the emergence of AI as an area of major significance in Computer Science. The module will be delivered at a level such that no prior programming experience will be required, and the topics will be examined at conceptual level so that participants will develop a good high level understanding of the wide ranging nature of AI, its various sub-areas and how they differ or relate to each other.
(Language of instruction: English)
Learning Outcomes
- Discuss the broad nature of AI and its underlying principles.
- Define a range of AI approaches and understand their conceptual design.
- Develop the ability to discuss, describe and understand AI concepts at a high level.
- Discuss the suitability of certain AI approaches for different problem domains.
- Develop written and verbal communication skills.
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5181: "Introduction to AI" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5182: Machine Learning and Natural Language Processing
CT5182: Machine Learning and Natural Language Processing
Semester 1 | Credits: 5
The first half of this module will introduce the concept of Machine Learning and look at some interesting applications. The theoretical aspects of the subcategories of Machine Learning (Supervised Learning, Unsupervised Learning, Semi-supervised Learning, Reinforcement Learning, and Deep Learning) will be studied in detail as well as common terminology associated with each. The second half will apply these machine learning techniques to problems in natural language, looking at the problems of text classification, annotation, translation and knowledge extraction. In addition, we will cover some useful linguistic fundamentals for understanding the challenges of natural language processing.
(Language of instruction: English)
Learning Outcomes
- Define Machine Learning and explain the major categories of learning task
- Outline common Machine Learning terminology associated with problem descriptions, experimentation, and performance analysis
- For each of the following, explain the requirements for the learning task and outline the types of problems that can be solved: Supervised Learning, Unsupervised Learning, Semi-supervised Learning, Reinforcement Learning, Deep Learning
- Outline the differences between classification, regression, clustering, and association providing examples for each
- Identify a suitable category of learning task for a given problem description
- Have a high-level familiarity with what algorithms are associated with particular learning tasks
- Name the main tasks in natural language processing
- Assess which NLP methodology is the best for a given task
- Analyse the requirements of NLP tasks and map them to existing systems
- Compose multiple NLP tasks to create solutions for complex problems
- Outline how NLP can solve real-world tasks
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5182: "Machine Learning and Natural Language Processing" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5184: Data Analysis and Visualisation
CT5184: Data Analysis and Visualisation
Semester 1 | Credits: 5
This module will cover analysis and visualisation to transform data into usable insight. It will teach simple visual methods of data summarisation. It will use graphical data visualisation tools for fast insight without using software coding. It will teach both principles and practice in order to avoid common visualisation errors which lead to inaccessible or misleading results.
(Language of instruction: English)
Learning Outcomes
- Design effective Data Visualisation according to Design Principles.
- Select and apply appropriate data visualisation methods.
- Carry out common data manipulations including pivots, dealing with missing data, and achieving tidy data.
- Use modern data tools to consume data in multiple formats.
- Select and apply appropriate data summarisation methods.
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "The Visual Display of Quantitative Information" by Edward R. Tufte
ISBN: 9781930824133. - "Storytelling with Data" by Cole Nussbaumer Knaflic
ISBN: 9781119002253.
Publisher: John Wiley & Sons - "Tableau Your Data!" by Daniel G. Murray
ISBN: 1119001196.
Publisher: John Wiley & Sons - "Exam Ref 70-778 Analyzing and Visualizing Data by Using Microsoft Power BI" by Daniil Maslyuk
ISBN: 9781509307029.
Publisher: Microsoft Press
Note: Module offerings and details may be subject to change.
RequiredCT5186: Future of Artificial Intelligence
CT5186: Future of Artificial Intelligence
Semester 2 | Credits: 5
This module aims to give learners an understanding of current trends in Artificial Intelligence (AI) and the future development of the field, covering both academic research and industrial deployments.
The focus of the module will be on the challenges and opportunities that developments in AI present to individuals, organisations and society.
Learners will gain experience of critiquing literature on AI and evaluating the technological readiness of various AI approaches to solve specific real-world problems.
(Language of instruction: English)
Learning Outcomes
- Demonstrate an awareness of future trends in AI and emerging AI technologies
- Identify opportunities to apply AI in specific problem domains
- Discuss the challenges associated with applying AI in specific problem domains
- Assess the technological readiness of a range of AI techniques to solve specific problems
- Critique literature on AI
- Communicate their knowledge of AI effectively through written reports, oral presentations and discussions
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5186: "Future of Artificial Intelligence" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5185: Ethics and Law for Artificial Intelligence
CT5185: Ethics and Law for Artificial Intelligence
Semester 2 | Credits: 5
This module introduces students to ethical and legal issues arising in the use of AI in professional settings. It covers considerations regarding privacy, algorithmic governance, fairness and bias, liability, surveillance, the use of artificial agents, contracting, and workplace issues. The module aims to facilitate informed reflection on these issues from ethical and legal perspectives, drawing on students' own personal and professional experiences.
(Language of instruction: English)
Learning Outcomes
- Explain the most significant ethical and legal questions that AI raises
- Apply relevant ethical and legal concepts in practice
- Apply the law to a range of situations involving the use of AI
- Critically evaluate suggested responses to ethical challenges and dilemmas
- Critically assess the various legal frameworks that are in place to address these questions
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Information and Communications Technology Law in Ireland" by Rónán Kennedy, Maria Helen Murphy
Publisher: Clarus Press - "Advanced Introduction to Law and Artificial Intelligence" by Woodrow Barfield, Ugo Pagallo
Publisher: Edward Elgar Publishing - "Three Liability Regimes for Artificial Intelligence: Algorithmic Actants, Hybrids, Crowds" by Anna Becker, Gunther Teubner
Publisher: Bloomsbury - "Oxford Handbook of Ethics of AI" by Markus Dubber, Frank Pasquale, Sunit Das (eds.)
Publisher: OUP - "Algorithms and Law" by Martin Ebers, Susana Navas
- "Automating Inequality: How high-tech tools profile, police and punish the poor" by Virginia Eubanks
Publisher: St. Martin's Press - "Data Protection and Artificial Intelligence" by Dara Hallinan, Ronald Leenes, Serge Gutwirth, Paul De Hert
Publisher: Hart Publishing - "Law for Computer Scientists and Other Folk" by Mireille Hildebrandt
Publisher: OUP - "Privacy and Data Protection Law in Ireland" by Denis Kelleher
Publisher: Bloomsbury - "Ethics of Artificial Intelligence" by Matthew Liao (ed.)
Publisher: OUP - "Augmented Exploitation: Artificial Intelligence, Automation and Work" by Phoebe Moore, Jamie Woodcock
Publisher: Pluto Press - "Privacy in Context: Technology, Policy, and the Integrity of Social Life" by Helen Nissenbaum
Publisher: Stanford University Press - "Algorithms of Oppression: How Search Engines Reinforce Racism" by Safiya Umoja Noble
Publisher: NYU Press - "An Introductory Guide to Artificial Intelligence for Legal Professionals" by Juan Pavón, María Jesús González-Espejo
Publisher: Kluwer Law - "Alone Together: Why We Expect More from Technology and Less from Each Other" by Sherry Turkle
Publisher: Basic Books - "The Oxford Handbook of Digital Ethics" by Carissa Veliz (ed.)
Publisher: OUP - "Regulating Artificial Intelligence" by Thomas Wischmeyer, Timo Rademacher
Publisher: Springer Nature - "The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power" by Shoshanna Zuboff
Note: Module offerings and details may be subject to change.
RequiredCT5212: Societal Impact of Artificial Intelligence
CT5212: Societal Impact of Artificial Intelligence
Semester 2 | Credits: 5
The module is designed to provide learners with a comprehensive understanding of the implications of advancements in AI for organisations and society. Throughout the module, learners will be exposed to various real-world examples, case studies, and discussions to understand the impact of AI on data, society, organizations and individuals. The course will also equip learners with the knowledge and skills to critically evaluate the social, ethical, and legal implications of AI and to contribute to the development of responsible and sustainable AI practices.
(Language of instruction: English)
Learning Outcomes
- Discuss the challenges and opportunities presented by applications of AI
- Critically appraise reports on current applications of AI
- Identify the social, ethical, and legal implications of AI advancements concerning data, society, organizations, and individuals.
- Evaluate whether a given AI application incorporates responsible and sustainable practices
- Communicate their knowledge of current AI applications effectively through written reports and discussions
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5212: "Societal Impact of Artificial Intelligence" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
- No experience in computer science or programming required: Learn about the key topics in AI from a high-level perspective. No prior qualification in computer science required. Ideal for learners who wish to be able to engage with AI experts, but who do not need to learn how to code AI systems or to become AI engineers.
- 100% online delivery: The full course can be completed from anywhere in the world, and it is not necessary to attend the university campus. Applications from learners based outside of Ireland are welcome.
- Study at your own pace: The course is delivered asynchronously, with no formal timetable. Study at your own pace with prerecorded lectures and course materials that are released weekly.
- 100% continuous assessment: The course does not have any formal end of year exams. Assessment is via 100% continuous assessment, where learners demonstrate what they have learned via a broad mix of assessment types, e.g. MCQs, essays, projects, presentations, discussion boards.
The PgCert in Artificial Intelligence for Professionals is targeted at learners who would benefit professionally from gaining a high-level understanding of the field of artificial intelligence (AI). Many of our learners are in mid or upper level management positions, and wish to upskill so that they can lead AI project within their own organisation or industry sector. The PgCert in Artificial Intelligence for Professionals is designed to address the needs of professionals who wish to upskill and learn the key concepts in AI, without needing to train as AI engineers.
How will I learn?
The MSc in Data Analytics combines innovative teaching methods with practical, hands-on learning to ensure a comprehensive educational experience. You will learn through a mix of interactive lectures, seminars and workshops led by expert faculty. Real-world case studies, data-driven projects and coding exercises will enable you to apply theoretical knowledge to practical problems.
Group projects and collaborative activities will enhance your teamwork and communication skills, while individual assignments and the final dissertation will help you develop independence and critical thinking.
Throughout the programme, you will have access to cutting-edge resources, including industry-standard software and real-world datasets, to support your learning and professional growth.
How Will I Be Assessed?
Throughout the programme, your progress is assessed through various coursework and exams, including reports, essays, presentations, and computer assignments.
- Continuous Assessment - Regular coursework, including essays, presentations, in-class tests, and language exercises. Students receive regular (weekly) feedback on their progress. (20%)
- Examinations - Written exams take place before Christmas and in May. Written and oral exams evaluate proficiency in grammar, vocabulary, comprehension, and communication. The Final Examination accounts for 50%.
- Project Work - Research and subtitling projects and translation assignments allow students to apply their skills in real-world contexts. (30%)
Course queries:
patrick.mannion@universityofgalway.ie
Programme Director(s):
Dr Patrick Mannion
Programme Director
School of Computer Science,
College of Science & Engineering
Accreditations & Awards
As a Development Manager with no AI background, I struggled to find a course that would give me a strong understanding of Artificial Intelligence without being overly technical or requiring coding. The AI for Professionals course at University of Galway was ideal - it covers AI, Machine Learning, AI Strategy, Ethics, Law, and Data Visualisation, providing students with the bigger picture of AI’s impact. Fully remote and flexible, with expert lecturers and a mix of essays, projects, and assessments, it equips managers to understand, build, and grow AI teams and solutions. Highly recommended.
Tom Lohan
Graduate
I highly recommend the PgCert (Computer Science—Artificial Intelligence for Professionals) at University of Galway. The course provides an in-depth education on AI, its current uses, emerging trends, and future developments. Assignments encourage students to explore topics in depth, research applications, and connect learning to real-world industry practice. Fully online with structured, realistic deliverables, weekly lectures, and interactive sessions, it equips graduates with a broad, practical understanding of AI techniques and their application across a wide range of sectors.
Lisa Molloy
Graduate
Meet our Employers
Entry Requirements and Fees
Minimum Entry Requirements
Applicants should hold a Level 8 honours undergraduate degree in any discipline. Applicants with a Level 7 ordinary undergraduate degree and relevant work experience will also be considered. Please email the Programme Director: patrick.mannion@universityofgalway.ie with any queries regarding entry requirements.
English Language Entry Requirements
Overall, entry to the MSc requires a minimum IELTS score of 6.5 overall, 6.5 in writing, with no less than 6.0 in any other band. TOEFL: Overall 88, Listening 12–19, Speaking 18–19, Writing 24–26, Reading 13–18. PTE: 61 overall, no less than 61 in writing, and no less than 50 in any one other component.
Supporting Documents
You will be required to provide supporting documentation as part of your application. You can check here what supporting documents are required for this course.
You can apply online to the University of Galway application portal here.
Please review the entry requirements set out in the section above.
You will be required to upload supporting documentation to your application electronically. See the section above on entry requirements for further information on the supporting documentation required for this course.
Closing Dates
For this programme, there is no specific closing date for receipt of applications. Applications will be accepted on a rolling basis and course quotes will be reviewed continuously throughout the application cycle.
Notes
- You will need an active email account to use the website and you'll be guided through the system, step by step, until you complete the online form.
- Browse the FAQ's section for further guidance.
Fees for Academic Year 2026/27
| Course Type | Year | EU Tuition | Student Contribution | Non-EU Tuition | Levy | Total Fee | Total EU Fee | Total Non-EU Fee |
|---|---|---|---|---|---|---|---|---|
| PG Certificate Part Time | 1 | €4,950 | €- | €4,950 | €- | €0 | €4,950 | €4,950 |
€2,995 for ITAG members; € 4,950 for non-members
Application Process
Students applying for full time postgraduate programmes from outside of the European Union (EU), You can apply online to the University of Galway application portal here.
Our application portal opens on the 1st October each year for entry the following September.
Further Information
Please visit the postgraduate admissions webpage for further information on closing dates, documentation requirements, application fees and the application process.
Why University of Galway?
World renowned research led university nestled in the vibrant heart of Galway city on Ireland's scenic West Coast.
Contact the College of Science and Engineering
University of Galway, University Road, Galway, Ireland, H91 TK33
Downloads
Meet Our Alumni
Course Introduction
An overview of key AI topics for working professionals
This programme focuses on the strategic and practical aspects of AI applications, rather the low-level details of how particular AI algorithms are implemented. Therefore, learners are not required to have a background in computer science, or to learn programming or advanced mathematics to succeed in this programme. Candidates will learn about the main AI techniques and what they can be used for, data and skills requirements for AI projects, case studies of AI systems, as well as the ethical and legal implications of AI applications for business and society.
