Data Science and Statistical Learning - MSc

During this programme, you will:

• Build core skills in statistical modelling, data visualisation, programming, database management, and predictive analytics.

• Transition into careers in data science and analytics across diverse industries with targeted, industry-relevant training.

• Get hands-on experience in scientific computation, data manipulation, interrogation, network analysis, and machine learning.

• Prepare for careers in diverse sectors such as tech, finance, pharmaceuticals, and research, or for progression to PhD programmes.

• Gain hands-on experience through a research project and MSc dissertation, with a focus on applied data science.

Key information:

• Complete full-time in one year

• Delivered on campus

• Modules taught during autumn and spring semesters

• Submit research project at the end of the summer semester

• There are two pathways available:
  • Pathway A is the main statistical learning and data science route, designed for most students.
  • Pathway B is intended for those who have already studied substantial statistical data science content as part of a previous degree (for example, UL mathematics graduates who followed the statistics route).

You will learn through a blend of:

• Lectures, workshops, and hands-on activities

• Reflective practice and guided research

• Regular feedback from faculty and peers

There are two pathways: Pathway A is the main statistical learning / data science pathway intended for the majority of students, while Pathway B is intended for students who have already covered significant statistical data science content in a prior degree (especially UL graduates of mathematics degrees who selected the statistics route).

Year 1

Autumn Semester

• Pathway A: Statistical Inference for Data Science (MS6051) develops fundamental inferential theory necessary for applying statistical methods in the field of data science.

• Pathway A: Fundamentals of Statistical Modelling (MS6061) equips students with the theoretical and practical knowledge of a range of statistical models applied to real-world data.

• Pathway A & B: R for Statistical Data Science (MS6071 )covers data wrangling, visualisation, dashboard creation, and statistical modelling in R, the programming language in statistical data science.

• Pathway A & B: Database Systems in Practice (CS6401) introduces the relational data model alongside NoSQL data models.

• Pathway A & B: Text Analytics and Natural Language Processing (EE6041) provides a practical knowledge of text analysis techniques and Natural Language Processing.

• Pathway B: Scientific Computation (MS6021) develops mathematical modelling techniques using differential equations and associated programming skills.

• Pathway B: Introduction to Data Engineering and Machine Learning (CE4051) provides an introduction into methods and software used in machine learning applications.

Spring Semester

• Pathway A: Statistical Learning (MS6022) grounds students in applied multivariate analysis techniques used in data science and statistical learning.

• Pathway A & B: Quantitative Research Methods for Science, Engineering and Technology (MS5052) prepares students for statistical research and develops core research skills.

• Pathway A & B: Networks and Complex Systems (MS6032) provides a well-rounded understanding in the application of network science methods.

• Pathway A & B: Applied Big Data and Visualisation (CS6502) introduces students to big data management and associated issues.

• Pathway A & B: Artificial Intelligence and Machine Learning (CS6514) develops skills in designing solutions for a variety of problems using multi-modal intelligent paradigms.

• Pathway B: Data Governance and Ethics (IN6062) provides a conceptual framework relating to governance and ethics in data analytics settings.

Summer Semester

• Pathway A & B: Research Project (MS6013) synthesises knowledge from taught modules to enables students to research and develop solutions for a relevant data science problem, culminating in a written dissertation and presentation.

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 Book of Modules

• Applicants should hold a bachelor’s degree (NFQ Level 8) with at least a second-class honour, grade 2 (2:2) in Mathematics, Statistics, Physics or a relevant quantitative discipline with a strong mathematical or statistical component.

• You must have and will only be considered if you have a strong foundation in mathematics from a prior programme of study, where they have excelled in their mathematical subjects.

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

• Previous experience of statistical programming is an advantage but is not required.

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

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.

TBC

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:

• ICT (e.g. Apple, Facebook, Google, LinkedIn, Microsoft, Tenable, TikTok)
• Financial services and management consulting (e.g. Accenture, AIB, Aon, Bank of Ireland, Deloitte, EY, KPMG, PWC, Zurich)
• Manufacturing and pharmaceuticals (e.g. Abbott, Eli Lilly, Glanbia, Johnson & Johnson, Regeneron)
• Research and development in applied fields

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