Data Science and Statistical Learning - MSc Programme content

Adapt your quantitative skills to data science and forge a career in ICT, financial services, management consulting, manufacturing and pharmaceuticals.

Key information

EU fees per year €8,200

Non-EU fees per year €20,800

NFQ Level NFQ Level 9 Major

Duration One year

Attendance Full-time

Programme type Taught

Delivery In-person

Start date September

Award Masters (MSc)

Apply Register your interest

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

Programme content

There are two pathways

Pathway A is the main statistical learning / data science pathway intended for the majority of students
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).

Pathway A - Core modules

R for Statistical Data Science (MS6071) covers data wrangling, visualisation, dashboard creation, and statistical modelling in R, the programming language in statistical data science.
Database Systems in Practice (CS6401) introduces the relational data model alongside NoSQL data models.
Text Analytics and Natural Language Processing (EE6041) provides a practical knowledge of text analysis techniques and Natural Language Processing.
Statistical Inference for Data Science (MS6051) develops fundamental inferential theory necessary for applying statistical methods in the field of data science.
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 B - Core modules

R for Statistical Data Science (MS6071) covers data wrangling, visualisation, dashboard creation, and statistical modelling in R, the programming language in statistical data science.
Database Systems in Practice (CS6401) introduces the relational data model alongside NoSQL data models.
Text Analytics and Natural Language Processing (EE6041) provides a practical knowledge of text analysis techniques and Natural Language Processing.
Scientific Computation (MS6021) develops mathematical modelling techniques using differential equations and associated programming skills.
Introduction to Data Engineering and Machine Learning (CE4051) provides an introduction into methods and software used in machine learning applications.

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.

Pathway A - Core modules

Quantitative Research Methods for Science, Engineering and Technology (MS5052) prepares students for statistical research and develops core research skills.
Networks and Complex Systems (MS6032) provides a well-rounded understanding in the application of network science methods.
Applied Big Data and Visualisation (CS6502) introduces students to big data management and associated issues.
Artificial Intelligence and Machine Learning (CE6002) develops skills in designing solutions for a variety of problems using multi-modal intelligent paradigms.
Statistical Learning (MS6022) grounds students in applied multivariate analysis techniques used in data science and statistical learning.

Pathway B - Core modules

Quantitative Research Methods for Science, Engineering and Technology (MS5052) prepares students for statistical research and develops core research skills.
Networks and Complex Systems (MS6032) provides a well-rounded understanding in the application of network science methods.
Applied Big Data and Visualisation (CS6502) introduces students to big data management and associated issues.
Artificial Intelligence and Machine Learning (CE6002) develops skills in designing solutions for a variety of problems using multi-modal intelligent paradigms.
Data Governance and Ethics (IN6062) provides a conceptual framework relating to governance and ethics in data analytics settings.

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.

Pathway A & B - Core module

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.