Social Data Science
Explore the principles and practices that underpin high quality, replicable quantitative social research using complex and large-scale data sources.
Key facts
Overview
This module introduces the foundations of high-quality, reproducible quantitative social research in a world shaped by complex and large-scale data. It focuses on how researchers practically work with, interpret, and communicate data.
Designed for students who want to engage with contemporary data-driven social research, the module brings together data management, data visualisation, and critical approaches to “big data,” equipping you to work with modern data sources responsibly.
A distinctive feature of this module is its emphasis on transparency, reproducibility and working with data responsibly. You will learn how to organise, document, and present your work so it can be understood and replicated by others. The module also encourages critical reflection on data, including questions of representation and the ethics of using data not originally collected for research.
Throughout the module, you will:
- assess the potential and limitations of large-scale data;
- develop reproducible workflows and analytical code;
- communicate insights through data visualisation;
- gain experience working with secondary data, including social media and administrative data sources.
This module is relevant to those working with social science data in various research settings, including government, private and third sectors.
Entrance requirements
A minimum of a second class honours degree (2.1 preferred) or equivalent with a quantitative methods component. Experience with R will be useful but not required. Applicants without these formal qualifications but with significant appropriate/relevant work/life experience are encouraged to apply.
English language requirements
If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
- IELTS Academic or UKVI 6.5 with a minimum of 6.0 in each sub-skill.
- Pearson Test of English (Academic) 62 overall with 60 in each sub-skill.
- TOEFL exams taken before 21 January 2026: 88 overall with 20 in reading, 19 in writing, 19 in listening and 22 in speaking.
- TOEFL exams taken from 21 January 2026: 4.5 overall with no less than 4 in any band.
See our information on English language requirements for more details on the language tests we accept and options to waive these requirements.
Objectives
On completion of this module you should be able to:
1. Critically appraise the value of 'big data' for use in social science research.
2. Demonstrate a solid understanding of how variables are operationalised and how they can be visualised.
3. Understand key principles of reproducible research and data management.
4. Produce visualisations for exploration, analysis and communication of statistical analysis.
Structure and content
Over eight sessions this online module covers key topics in working with social science data. These includes: principles of data management; principles of data visualization; understanding how we can use ‘big data’ for social research; the ethics of working with big data for social research; analytical challenges arising with big data for social research; practical demonstrations.
Delivery and assessment
The module runs in a flipped-classroom format, combining video lectures with weekly discussion seminars and practical sessions requiring active engagement and discussion with peers. The assessment is a coursework portfolio.
Module coordinator
What next?
Contact us
If you have any questions about entry requirements for our continuing professional development and short courses, contact our Admissions team.
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