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During this programme, you will:  

• Acquire practical skills such as data interrogation, visualisation, and predictive analytics using the programming language R. 

• Produce interactive digital dashboards and visualisations with R-Shiny. 

• Improve critical decision-making using data-driven models and employ modelling to predict future trends in your organisation. 

• Develop transferrable skills that enable you to highlight your knowledge and explore what your career might look like in the future, understand the relationship between critical thinking and problem solving in the workplace, and cultivate professional writing skills for effective communication to stakeholders. 

Key information: 

• Complete part-time over one year  

• Delivered online 

• Intakes in both Autumn (September) and Spring (January) 

• Modules taught during autumn and spring or spring and autumn semesters   

• Approx. 12 weeks per semester 

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

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

You will learn through a blend of:  

• Recorded lecture material, 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 

• No requirement to be on campus 

• Additional prep and group work time  

• Timetable for relevant evening sessions provided after registration 

Year 1  

Spring Semester 

• Data Analytics with R (M) (MA5021) introduces programming in R and RStudio for data analytics, covering data wrangling, visualisation, basic statistical modelling, and reproducible research practices, with an introduction to RShiny dashboards. 

• introduces core concepts in statistics and data analytics, focusing on real-world applications in industry and equipping students with practical skills in experimental design, data analysis, and statistical programming using R. 

• Choose One of:
  • fosters critical thinking, creativity, and collaboration, enabling students to navigate complexity and uncertainty in professional contexts through the development of a portfolio that demonstrates the application of futures thinking methodologies to real-world challenges.
  • introduces critical thinking and its relationship to problem solving by challenging the way we think and approach problem solving, identifying and analysing problem dilemmas through a critical thinking lens, and reflecting on how individual approaches to critical thinking relate to problem solving in the workplace. 

Autumn Semester 

• focuses on the development and application of linear and generalised linear models to real-world data, emphasising model fitting, selection, interpretation, and communication, with practical implementation in R. 

• provides a foundation in applied statistical learning, introducing key supervised and unsupervised techniques, such as classification, clustering, and dimension reduction, with hands-on implementation in R for practical data analytics applications. 

• One of:
  • supports independent, self-directed learning through the creation of a personal portfolio that showcases reflective practice, the applied use of discipline-specific knowledge, and leadership in shaping the future of one’s professional role.
  • develops professional writing skills to effectively communicate inherently challenging content in an easy-to-digest way.  

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 typically hold a bachelor’s degree ( Level 8) with at least a second-class honour, grade 2 (2:2) in a related discipline. 

and/or 

• You should have at least 5 years of relevant industry or workplace experience. 

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: 

• *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 - €3,500 

Non-EU €4,750

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 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 

Springboard - Candidates who satisfy the eligibility criteria under Springboard+ can qualify for 90% or 100% funding subject to the availability of places. To clarify eligibility please go to please note that this is only available on the September Intake of the programme. 

Find further information on funding and scholarships.

“The course was challenging but incredibly well-designed. We had online classes, and the assignments were opportunities to practice what we learned. The professors were always available to support us. I took it very seriously and learned so much. I stated on my LinkedIn profile that I was completing a course in data analytics ��ɫ��Ƶ, and I was looking for work. So many recruiters reached out to me and offered me jobs. I could actually choose from the many options available.” Read Diego’s story here 

“I wanted change as I’m in the same job and dept for 15/16 years. Since I added this course to my LinkedIn profile, I’m being contacted by recruiters at least once a week for Business Analytics (BA) roles because all employers now want DA skills as part of it. BA and PM roles now need these skills, and they recognise it now. Amazing the interaction since I put this course on my LinkedIn. Originally, I said I wanted to move out, but this has really reinvigorated my current role.” 

“The way the course is designed it’s led by the tools. I found the modules good; DA has been good, the tools and the pace has been good, self-learning is good not time-constrained, do it at your own time. A very flexible approach that works well, lecturers have been v approachable. It almost feels like a regular classroom with their availability through email, or a scheduled Teams call. These short courses are a great benefit to industry.” 

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