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Accessibility & LanguageProfessional Diploma in Practical AI for Software Developers (part-time)
Professional Diploma in Practical AI for Software Developers (part-time)
College of Science and Engineering, School of Computer Science- Title of Award
- Professional Diploma
- Delivery
- On Campus
- NFQ
- Level 9
- Award Type
- Major
- Duration
- 1 year, part-time
- ECTS Weighting
- 30
Why Choose This Course?
Course Information
Who is this course for?
AI is reshaping the landscape of software development resulting in rapid advancement that will redefine the industry. A recent study by Gartner, a US based tech research firm, predicts that by 2027, 80% of software engineers will need to upskill to remain relevant in the increasing AI driven world. To fill this gap, the Professional Diploma in Practical AI for Software Developers, provides developers with advanced AI skills necessary to build and deploy modern artificial intelligence in customer facing projects.
This Professional Diploma is designed to equip software professionals with advanced competencies, essential for the rapid adoption and integration of AI technologies in software development workflows.
This one-year, fully online, part-time programme includes machine learning, an overview of Programming & Tools for AI, the Future of AI and hands-on training on AI model deployment. Learners will gain industry-relevant experience through hands-on AI model development and deployment which includes testing, debugging, and adopting AI-driven DevOps and CI/CD pipelines. Project-based learning will ensure participants acquire skills that are aligned with industry best practices, enhancing their employability in an AI-driven software landscape.
What will I study?
The Professional Diploma in Practical AI for Software Developers is a Part-Time, 1-year 30-ECTS course, consisting of 3 Semesters.
In Semester 1 you will study 2 modules:
- Principles of Machine Learning (5 ECTS)
- Programming and Tools for Artificial Intelligence (5 ECTS)
In Semester 2 you will study 2 modules:
- Future Of Artificial Intelligence (5 ECTS)
- AI Model Deployment (Continuous Assessment) (5 ECTS)
Semester 3 consists of a Capstone AI for SW Project (10 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)
RequiredCT5230: Capstone AI for SW Project
CT5230: Capstone AI for SW Project
15 months long | Credits: 10
The module serves as a bridge between academic knowledge and practical application, preparing students for seamless entry into the professional sphere, either through hands-on experience in industry or intensive team work in capstone project. The module promotes the research based and team based practical learning.
(Language of instruction: English)
Learning Outcomes
- Identify and research an artificial intelligence problem
- Implement an artificial intelligence application in software
- Identify, describe, and synthesize the state of the art approaches to an AI problem from a software development perspective.
- Develop an efficient model deployment pipeline
- Create a fully functional Artificial Intelligence based demo application or service
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 CT5230: "Capstone AI for SW Project" and is valid from 2025 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5229: Programming and Tools for Artificial Intelligence (CA)
CT5229: Programming and Tools for Artificial Intelligence (CA)
Semester 1 | Credits: 5
This module is about programming and computational tools required for artificial intelligence. It uses the Python language as the main vehicle, but focusses on conceptual material rather than just the language itself. It moves fast through introductory Python workings. It covers several important Python libraries in detail, especially for numerical computing, machine learning, plotting, graphs. It discusses approaches to building re-usable, high quality code but not software engineering per se. It also visits some extra topics such as version control and introduction to the R language for statistics. The module is core for the University of Galway MSc in Artificial Intelligence (MScAI) Part-time (online) and Full-time (classroom). The syllabus and assessment will be the same for both.
(Language of instruction: English)
Learning Outcomes
- Read and write simple Python programs, e.g. for data munging, with a high degree of comfort.
- Use R for simple statistics and data exploration.
- Use numerical Python libraries for manipulation, input/output, visualisation of numerical data using Numpy array types.
- Use essential tools for AI, including libraries for data gathering, numerical computing, machine learning, combinatorial programming, and modelling networks.
- Plan/design a program using any of the above facilities; test it; document it; execute it locally or in the cloud as appropriate.
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
- DEIRDRE KING 🖂
- GERALDINE HEALY 🖂
- JAMES MCDERMOTT 🖂
- Adrian Clear 🖂
- Mamoona Asghar 🖂
- Joseph Lemley 🖂
- Priyanka Verma 🖂
Reading List
- "A Whirlwind Tour of Python," by Jake Vanderplas
- "Think Python 2nd edition" by Allen B. Downey
Note: Module offerings and details may be subject to change.
RequiredCT5228: Principles of Machine Learning (CA)
CT5228: Principles of Machine Learning (CA)
Semester 1 | Credits: 5
Machine Learning is concerned with algorithms that improve their performance over time, as they are exposed to new data. This module introduces learners to the different categories of machine learning tasks and provides in-depth coverage of important algorithms for tackling them. Its focus is on the theory underlying ML algorithms. In addition, the learners gain experience of implementing algorithms from scratch, as well as using ML software tools to select and apply these algorithms in applications, and they evaluate and interpret the results.
Topics include:
1. Overview of Machine Learning & Major Categories of Task
2. Supervised Learning Principles and Information-Based Learning
3. Similarity-Based Learning
4. Evaluating Classifier Performance, Practical Advice, and Some Machine Learning Tools
5. Linear Regression in One and Multiple Variables
6. Linear Classifiers with Hard and Soft Thresholds
7. Probabilistic Machine Learning
(Language of instruction: English)
Learning Outcomes
- Define Machine Learning and explain what major categories of learning tasks entail
- Demonstrate how to apply the machine learning and data mining process to practical problems
- Explain and apply algorithms including decision tree learning, instance-based learning, probabilistic learning, linear regression, logistic regression, and others
- Select, apply and evaluate appropriate algorithms, and interpret the results on a given dataset and task.
- Discuss ethical issues and emerging trends in machine learning.
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 CT5228: "Principles of Machine Learning (CA)" and is valid from 2025 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5227: AI Model Deployment (CA)
CT5227: AI Model Deployment (CA)
Semester 2 | Credits: 5
This module provides learners with a practical understanding of the methods, tools, and challenges involved in integrating AI and machine learning models into customer facing products from a software developer perspective. Efficient deployment of pre-trained machine learning models across a variety of environments including cloud services, web applications, mobile devices, embedded systems, and PCs will be taught.
(Language of instruction: English)
Learning Outcomes
- Describe a number of fundamental ML and AI models, to reduce them to their basic operations, and to be capable of estimating their computation and memory requirements.
- Demonstrate an awareness of the platforms and technologies that enable the deployment of AI models in public facing software and products.
- Analyze and evaluate AI model architectures against system and platform requirements to identify and address compatibility and performance issues.
- Quantify, and compare the security, privacy, data and environmental implications of public facing AI models and select options to mitigate them where possible.
- Implement pre-trained models on a variety of target platforms such as edge devices, PCs, mobile devices, and web applications.
- Design and develop a comprehensive AI deployment pipeline that integrates one or more pre-trained AI models and platforms while addressing performance, security, and operational challenges.
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 CT5227: "AI Model Deployment (CA)" and is valid from 2025 onwards.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.
- Fully online including labs, lectures and assessment.
- Part-time with labs taking place outside of normal working hours.
- Practical approach allows applicants to learn in-demand AI skills.
- Provides students with up-to-date knowledge on how to deploy real AI models in the industry.
- As a Springboard+ funded course, most or all the fees are covered, subject to eligibility: HEA - Springboard+
Graduates of this programme will be equipped with in-demand AI skills, that they can apply within their existing roles as software developers. Furthermore, the Professional Diploma in Practical AI for Software Developers opens the door to diverse career pathways across various industries. The key career pathways include:
- AI Software Engineer
- Machine Learning Engineer
- Data Scientist
- AI Product Developer
- Embedded AI engineer
- Mobile AI Engineer
- AI Solutions Architect
- MLOps Engineer
- Dr. Joseph Lemley - Programme Director
- Dr. Priyanka Verma - Programme Director
- Dr. James McDermott
- Dr. Ihsan Ullah
- Dr. Patrick Mannion
How will I learn?
Online lectures/labs/tutorials comprise approx 8 class hours per week during the evenings Monday-Friday. All lectures are pre-recorded and made available online to registered students via the University's virtual learning environment. Some modules include labs which are scheduled in the evening with direct interaction and feedback from the instructor (also via the University's virtual learning environment).
Efforts are made to ensure that these live labs are accessible and at times that permit people in full-time employment to fully participate. Exams are not done in the classroom but will be live and scheduled in the evenings; a web-based proctoring tool is used for exams which take place online so students will undertake them remotely in their own home.
Course queries:
Gail Cassidy
ICT Skills Conversion Courses Administrator
Centre for Adult Learning & Professional Development
University of Galway
Galway
E: ictskills@universityofgalway.ie
T: 091-495241
Programme Directors:
Dr Joseph Lemley
Lecturer
School of Computer Science
College of Science and Engineering
E: Joseph.lemley@universityofgalway.ie
&
Dr Priyanka
Lecturer
School of Computer Science
College of Science and Engineering
E: Priyanka.Verma@universityofgalway.ie
University of Galway recognises that knowledge and skills can be acquired from a range of learning experiences. This is in line with the National Framework of Qualifications (NFQ) goals which aim to recognise all learning achievements by supporting the development of alternative pathways to qualifications (or awards) and by facilitating the recognition of prior learning (RPL).
Recognition of prior learning is facilitated automatically during the application phase.
Graduates of the Professional Diploma in Practical AI for Software Developers will be able to:
- Apply appropriate machine learning & artificial intelligence algorithms to real-world problems.
- Gain the ability to quantify and compare the security, privacy, data and environmental implications of public facing AI models and select options to mitigate them where possible.
- Obtain advanced skills in integrating and deploying AI models in customer facing software platforms.
- Obtain a broad, practical grounding in the field of artificial intelligence.
- Design and develop a comprehensive AI deployment pipeline that integrates one or more pre-trained AI models and platforms while addressing performance, security, and operational challenges.
- Critically assess and synthesize AI research for software development, demonstrating the ability to communicate findings effectively through written and oral presentations.
Accreditations & Awards
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Entry Requirements and Fees
Candidates must hold at least a Second-Class Honours Level 8 primary degree in a related subject area or hold a primary degree in a related area (which is acceptable to the college) without honours and have three years’ relevant practical experience in the subject area.
Academic entry requirements standardised per country are available here.
Candidates must hold a Level 8 qualification (or equivalent) with at least a Second Class Honours from a recognized university or college in a numerate field and must possess programming experience.
Candidates with a Level 7 qualification can also apply if they can show they have gained Level 8 skills through at least two years of work experience in IT or a tech-related role.
Important: This is a Springboard+ course and admission is conditional on meeting Springboard+ requirements available here: HEA - Springboard+
You can apply for the Professional Diploma in Practical AI for Software Developers online via Springboard+:
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 Date: July 18th, 2025.
International Applicants
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 until 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.
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 Diploma part Time | 1 | €4,100 | €70 | €4,170 |
As a springboard+ funded course, 90% of the fees are paid for those in employment and 100% of fees are paid for the unemployed subject to springboard eligibility requirements.
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
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Course Introduction
Foundations for a career in today’s software industry
This professional diploma providers learners with the hands-on experience and practical skills necessary for software developers to understand, build, and deploy modern artificial intelligence in customer facing projects. This online programme is designed to accommodate the needs of those in full time employment to upskill themselves with AI in the evolving tech landscape.
This course will equip software developers with advanced AI skills through practical training.
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