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During this programme, you will:  

• Build advanced skills in financial data analysis, machine learning, and AI-driven decision-making. 

• Learn to interpret complex financial datasets and generate high-value insights for strategic decision-making. 

• Apply statistical and computational techniques to real-world financial problems, including risk assessment, trading, and investment analytics. 

• Develop innovative solutions using deep learning and other AI tools tailored to the financial services sector. 

• Strengthen your critical thinking, problem-solving, and leadership abilities in both digital and in-person environments. 

• Join a community of professionals committed to ethical practice and lifelong learning in finance and data science. 

Key information: 

• Complete part-time in one year   

• Delivered online with a mix of pre-recorded and live lectures

• Modules taught during autumn, spring and summer semesters 

• Approx. 12 weeks per semester 

• Modules with (M) beside them can be taken as independent micro-credential  

• Can be used to complete 30 credits towards Master of Professional Practice (MPP) or MSc Engineering Practice 

You will learn through a blend of:  

• Lectures, workshops, and hands-on activities  

• Reflective practice and guided research  

• Regular feedback from faculty and peers  

Part-time considerations:  

• Designed for working professionals  

• Evening or recorded lectures  

• Additional prep and group work time  

• Timetable provided after registration 

Year 1 

Autumn Semester 

• equips you with the skills to analyse data using both descriptive and inferential statistical techniques. You will also learn how to critically evaluate the analyses of others, strengthening your ability to interpret and communicate data insights. 

• offers a practitioner-focused introduction to machine learning algorithms used in capital markets and asset management. You will gain hands-on experience applying these techniques to real-world financial scenarios within the international financial services sector. 

Spring Semester 

• provides practical, industry-focused skills in deep learning algorithms. You will explore artificial neural networks, a key subfield of machine learning, and learn how these brain-inspired models are used to solve complex financial problems. 

• introduces the latest concepts and techniques in derivatives, helping you understand the economic principles behind their pricing and use. You will also explore how derivative knowledge can be applied in machine learning contexts to enhance financial decision-making. 

• prepares you to think strategically about the future by exploring futures thinking and scenario planning. You will develop your creativity, critical thinking, and collaboration skills to navigate uncertainty and complexity in professional environments. 

Books and journal articles needed for the course will be available online through the UL Glucksman Library.  

For more information on each module, you can search the faculty, school and module code on UL’s  

• Applicants should hold a bachelor’s degree ( Level 8) with at least a second-class honour, grade 2 (2:2) in a finance-related discipline which demonstrates relevant numerate experience in; finance analytics, economics, business, engineering, computing, mathematics, science, or technology. 

• You will also be considered if your primary bachelor’s degree is not finance related but has a relevant mathematics and computing element.  

• You will also be considered if your bachelor’s degree is from a non-numerate discipline and you have a minimum of 3 years of experiential learning in an appropriate computing discipline. 

• The university may shortlist and invite you to an interview. 

Other Entry Considerations: 

We encourage you to apply even if you don’t meet the standard entry requirements, as long as you can show that you have the knowledge, skills, and experience needed for the programme. 

At UL, we value all kinds of learning and support different ways to qualify through our Recognition of Prior Learning (RPL) policy.  

International students:   

For details on country-specific qualifications visit postgraduate entry requirements for international students. 

Checklist of documents:    

For all applicants:   

• *Academic transcripts and certificates
  • UL graduates only need to provide their student ID.
• Copy of your birth certificate or passport 
• English translation of your qualifications and transcripts
• Copy of your CV 

English Language: 

• English Language Competency certificate
• For details on accepted language qualifications visit English Language Requirements 

Guidelines on Completing your Application   

• To make sure we can review your application quickly, please:
  • Upload all documents. Your application can’t be reviewed until we have all the documents on the checklist. 
  • Title the documents you are uploading. For example, "Personal Statement", "Undergraduate Transcript", "Postgraduate Transcript", "English Language Certificate" etc. 
• *If you are waiting to graduate, submit your application with the documents you have to date, you don’t need to have finished final exams before applying. 

EU -€5,250

Please note that international study visas are only available to students studying full-time in Ireland. This programme does not qualify for a study visa. 

Annual fees are billed by semester. Once registered, students may be eligible to apply for a monthly payment plan.  

Further information on fees and payment of fees is available from the Student Fees Office website. All fee related queries should be directed to the Student Fees Office (Phone: +353 61 213 007 or email student.fees.office@ul.ie).  

 
Funding 

Find further information on funding and scholarships.

This course can lead to the following sectors and careers:  

• Tax careers in the public and private sectors
• Financial Management roles
• Fund management career paths
• A variety of financial analytics positions  

Still Curious?  

The team regularly host and take part in webinars to support future students. If you would like to learn more or ask questions at an online information session, click below.     

Online Information Sessions