Quantitative Methods Pathway
Change the World with Data
In an age of algorithms, social platforms, and endless data streams, the social sciences needs data-savvy thinkers more than ever. The Quantitative Methods Pathway (SQM) is your opportunity to build the skills to make sense of it all. Whether you want to shape policy, tackle inequality, or understand digital behaviour, this pathway gives you the tools to turn numbers into knowledge.
Why Choose This Pathway?
- Gain cutting-edge quantitative research and data analysis skills
- Learn to work with large-scale real-world datasets
- Master industry-standard software.
- Receive personal support in small-group, hands-on teaching environments
- Build a portfolio of data projects valued by employers
- Convert your BA degree to a BSc with Quantitative Methods
Graduates from this pathway have gone on to careers in fintech, government research, NGOs, market research, public health, and postgraduate study.
I added SQM to my degree in second year without knowing anything about statistics, but the modules gave me the skills and confidence to learn it from scratch. The support from staff and classmates was invaluable, and it ultimately allowed me to change my degree from a BA Politics to a BSc Politics with Quantitative Methods. The SQM modules provided a strong foundation in statistical analysis and problem-solving, which helped me secure my role as an Assistant Statistician and feel prepared for this next step in my career. Nicole Beck
BSc Politics with Quantitative Methods
Assistant Statistician, NISRA, Belfast
My time on the course was so rewarding. I developed practical skills in my quantitative methods modules that were transferable and directly applicable to the workplace. Hannah Keenan
BSc Sociology with Quantitative Methods
EU Venue Governance Officer, TP ICAP Group, Paris
What You’ll Learn
You’ll combine training in your core social science discipline with advanced quantitative skills. Through data labs, projects, and practical assignments, you’ll explore major social questions and learn how to:
- Design effective surveys and collect reliable data
- Manage and analyse large-scale social datasets
- Communicate complex results using impactful data visualisations
- Use statistical models to uncover patterns in data, test theories and answer key social science research questions
How It Works
- Choose SQM modules as your optional modules in level 2 and 3 (1 module per semester in year 2 and 3)
- Develop key transferable skills working in data labs using specialist software? No previous experience required - we start with the basics
- Open to students on the following degree programmes, (SH: single honours; JH: joint honours):
- School of Social Sciences, Education and Social Work: Criminology (SH); Sociology (SH); Criminology and Social Policy (JH); Criminology and Social Policy (JH); Social Policy and Sociology (JH).
- School of History, Anthropology, Philosophy and Politics: International Relations and Conflict studies (SH) and Politics (SH)
Teaching is practical, well-paced, and highly supportive. You’ll get: one-on-one help when you need it; a friendly learning environment with small class sizes; direct access to experienced, approachable staff. The SQM pathway provides a core foundation in social science data analysis and qualitative methods and is an excellent base for further postgraduate training in data science and statistics.
No Maths A-Level? No Problem
This pathway is ideal for students who want to: Learn to analyse data (from scratch!); Gain practical quantitative research experience; Improve job prospects in a competitive market
You don’t need prior experience in statistics or coding. Just bring your curiosity and a willingness to learn.
Ready to Join?
The Quantitative Methods Pathway is your launchpad into the world of data-driven social science. Whether you’re passionate about people, policy or patterns, this pathway will give you the skills and confidence to make your mark.
Contact us to learn more or talk to a student currently on the pathway.
Dr Emma Calvert: e.calvert@qub.ac.uk
Dr Cate McNamee: c.mcnamee@qub.ac.uk
Prof Andrew Percy: a.percy@qub.ac.uk