Program Overview

Overview of the M.Sc. in Applied Social Data Science

The M.Sc. in Applied Social Data Science is a one-year, full-time programme designed for students with a strong interest in the social world and a desire to learn how to effectively apply cutting-edge quantitative methods to solve real-world political, economic, and social problems.

Course Details

• Awards: NFQ Level 9
• Number of Places: 30 Places
• Next Intake: September 2025
• Course Coordinator: Mr Michael Enearu
• Course Director: Dr Jeffrey Ziegler/Dr. Tom Paskhalis
• Closing Date: 31 July 2025

Admission Requirements

• Admission to the course is competitive. Applicants will be expected to hold an upper second class honours (2:1) degree or higher from an established university.
• A background in a social science discipline or computer science, statistics, or other related discipline will be an advantage but is not required.
• GPA scores of at least 3.3 out of 4, or equivalent, will be expected from international applicants.
• English Language Requirements: All applicants to Trinity are required to provide official evidence of proficiency in the English language. Applicants to this course are required to meet Band B (Standard Entry) English language requirements.

Course Structure

The programme covers a wide range of core modules which will help students expand their quantitative research methods toolkit.

• First Semester: Modules in computer programming, applied statistical analysis, and research design.
• Second Semester: Modules in machine learning, forecasting methods, and quantitative text analysis.
• Dissertation: A key piece of research related to the field of study over the summer months.

Career Opportunities

The M.Sc. in Applied Social Data Science award from Trinity is expected to provide students with a competitive edge in the rapidly expanding job market for data scientists.

• Students will acquire valuable skills which are sought after in the high productivity sectors, including information technology, consulting, financial services, risk-assessment, logistics, as well as positions in government, NGOs, and academia.
• Students will become fluent in two very popular (and demanded) programming languages, R and Python.
• Students will gain working knowledge and application experience in advanced statistical analysis, machine learning, and important applications such as social forecasting and quantitative text analysis.

Course Fees

For a full list of postgraduate fees, please refer to the relevant section. The Department is not in a position to offer scholarships/fee waivers to incoming students; we recommend investigating funding/scholarship options before making an application.

Research Areas

The programme combines rigorous training in methods from computer science and statistics with social science research skills. There is high demand for social scientists who can apply machine learning and statistical methods for data-driven solutions to challenges faced by governments, political groups, and civil society organizations.

• Political organizations seek employees who can use computational approaches to analyze large-scale digitized collections of political communication.
• Government agencies seek employees with experience in applying statistical forecasting methods to predict international conflict, terrorism, and disease outbreaks.
• Non-profit organizations are keen to hire people who are able to apply machine learning and natural language processing methods to help create data-driven solutions for disabled people.