Social Data Science

This programme is suitable for individuals with a background in the social sciences, arts & humanities, psychology, business, or STEM disciplines.



Applicants with minimal or no technical experience will explore a variety of computational methods and gain the programming skills needed to apply them in professional contexts.



Applicants with STEM backgrounds will be introduced to social science practice and the unique conceptual and methodological challenges of applying data science methods in the social domain.



The programme does not intend to provide advanced training in computational methods. Individuals with strong technical skills interested in furthering their technical competence are encouraged to explore alternative programmes in the Schools of Computer Science or Mathematics and Statistics.

Stage 1 Core Modules



SOC40640 Social Simulation: Methods and Models Autumn 10

SOC41200 Research Design Autumn 10

SOC41130 AI and Society Spring 10

SOC41220 Social Networks Spring 10



Stage 1 Options - A)20CR:



Students must choose a maximum of 20 credits of which 10 credits must be at level 4 from the list of options. Students must select 10 credits in the Autumn and 10 credits in the Spring Trimester from the list of Options. The maximum amount of credits allowed per one trimester is 30.



COMP10010 Introduction to Programming I Autumn 5

COMP40610 Information Visualisation Autumn 5

COMP47340 Computational Thinking (Conversion) Autumn 5

COMP47460 Machine Learning (Blended Delivery) Autumn 5

GEOG40770 GIS for Environmental Assessment Autumn 5

GEOG40820 GIS Principles and Applications Autumn 10

POL40950 Introduction to Statistics Autumn 10

POL42540 Applied Data Wrangling and Visualisation Autumn 5

SOC41160 Global Solutions and Applied Social Change Autumn 10

SOC41180 Global Responses to Climate Change Autumn 10

SPOL40470 Comparing Healthcare Systems Autumn 10

STAT30340 Data Programming with R (Blended) Autumn 5

STAT40400 Monte Carlo Inference Autumn 5

COMP47670 Data Science in Python (MD) Autumn and Spring (separate) 5

COMP47750 Machine Learning with Python Autumn and Spring (separate) 5

COMP10020 Introduction to Programming II Spring 5

COMP30110 Spatial Information Systems Spring 5

CSOC30030 Advanced Computational Social Science Spring 5

ECON42720 Causal Inference & Policy Evaluation Spring 5

POL42050 Quantitative Text Analysis Spring 10

POL42340 Programming for Soc Scientists Spring 10

SOC40330 Workshop in Qualitative Research Spring 10

SOC40620 Nationalism and Social Change Spring 10

SOC40670 Global Migration Spring 10

SOC40720 Organised Violence and Society Spring 10

SOC40790 Art, Knowledge & Social Change Spring 10

SOC41120 Human Development Challenges in the Global South Spring 10

SOC41150 Queering Global Challenges Spring 10

SOC41170 R.A.G.E. - Remembrance, Activism, Genocide, Emotions Spring 10



Stage 1 Options - B) Min 1 of:

Students must either take SOC40140 OR, alternatively both SOC41010 AND SOC41020. A maximum of 30 credits in total must be chosen from this section.



SOC40140 Dissertation Summer 30

SOC41010 Capstone Research Project Summer 15

SOC41020 Internship Summer 15

Applicants will be required to hold a 2.1 Honours degree or equivalent in a computational science discipline, and to show evidence for strong interest in social science research; Or to hold a 2.1 Honours degree or equivalent in a social science disciplines and to show evidence for strong interest in obtaining, or existing computational skills. Additionally, excellent academic references are required.



This degree programme includes modules from Computer Science, which involves logical understanding and reasoning and therefore applicants must be able to demonstrate good evidence of algorithmic thinking.



All applicants will be assessed on a case-by-case basis and relevant work experience will be taken into account, so that in certain cases an award at a 2.2 classification may be considered.



Students whose first language is not English will need a recognised English language qualification. On the International English Language Testing System (IELTS) students will need to achieve an average score of 6.5 over all components and a minimum of 6.0 in each band on the Academic Version. More details are available through the UCD International Office at http://www.ucd.ie/international/study-at-ucd-global/ucdenglishlanguagerequirements/



These are the minimum entry requirements – additional criteria may be requested for some programmes



You may be eligible for Recognition of Prior Learning (RPL), as UCD recognises formal, informal, and/or experiential learning. RPL may be awarded to gain Admission and/or credit exemptions on a programme. Please visit the UCD Registry RPL web page for further information. Any exceptions are also listed on this webpage. https://www.ucd.ie/registry/prospectivestudents/admissions/rpl/

Apply online.



Who Should Apply?

Full Time option suitable for:

Domestic(EEA) applicants: Yes

International (Non EU) applicants: Yes



Part Time option suitable for:

Domestic(EEA) applicants: Yes

International (Non EEA) applicants: Yes

1/2 years, FT/PT, On Campus.

W559 MSc Social Data Science Master of Science Full-Time Commencing September 2026 Graduate Taught



W560 MSc Social Data Science Master of Science Part-Time Commencing September 2026 Graduate Taught

The MSc in Social Data Science is ideal for individuals who want to avail of the excellent employment opportunities created by the progressive digitalisation of society.



Most graduates from our programme have gone on to pursue successful careers as analysts or data scientists with national and international private-sector organisations, government agencies, and NGOs, such as:



Accenture

Deloitte

Central Statistics Office

Bank of Ireland

Workday

Department of Social Protection

Higher Education Authority

IBM

PwC

Capgemini



Some graduates have also continued into PhD programmes in a variety of disciplines, both at UCD and abroad.