Module code: SOCM081

Module Overview

We live in a world where large quantities of data are regularly collected about people, institutions, and social structures. This module will enable students to understand how data science techniques can be used to leverage this data and answer complex questions about the social world. Questions like ‘why some people are more at risk of crime than others?’, ‘what explains differences in life expectancy between countries?’, and ‘do gender inequalities persist in the workplace’.


Throughout the module, students will focus on the underlying principles and uses of some of the core data science approaches rather than on the mathematical and statistical theory. Students will therefore develop a solid empirical grounding to be able to critically evaluate the findings from a wide range of quantitative social science research. In the accompanying workshops students will get hands on experience with a number of different social data science approaches in R covering regression approaches for theory testing, machine learning and prediction models, and matching algorithms for quasi-experimental designs. Students will also get a good understanding of how to interpret the results from these approaches and how they can be applied in practice.

Module provider


Module Leader

BRUNTON-SMITH Ian (Sociology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 11

Independent Learning Hours: 106

Lecture Hours: 11

Guided Learning: 11

Captured Content: 11

Module Availability

Semester 2

Prerequisites / Co-requisites

Students must have completed SOCM064 (Social data analytics), or be able to demonstrate that they have received an equivalent level of quantitative methods training

Module content

This module elaborates on social science approaches to data science, combining this with practical model building experience and critique using R. Indicative content includes:


  • Designing and building data science models to answer social science questions

  • Querying generative AI chatbots for code enhancements, model troubleshooting, and synthetic data generation.

  • Model testing and cross-validation

  • Sensitivity analyses

  • Interpreting results and finding the narrative


Hands on practical workshops will provide students with experience of:


  • Generalised Linear Models

  • Machine Learning algorithms

  • Matching approaches and treatment effect estimation

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Analytic Report 100

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

A detailed understanding of how to use data science approaches to answer questions about the social world. Using real data, students will apply a number of different techniques (regression, machine learning, matching) requiring them to make appropriate decisions based on particular data constraints, and assess the robustness of models. They will also demonstrate their ability to interpret the findings from these models, and discuss their implications.

Thus, the summative assessment for this module consists of:

An analytic report (4,000 words) where students will conduct a data science analysis and write up their results. 

Formative assessment and feedback 

Students will be able to complete workshop exercises during the module, giving them the chance to discuss the answers with each other and the lecturer. They will also have the opportunity to discuss their assignment plans in detail with the lecturer and will need to provide an initial scoping plan for feedback prior to completing the assignments. The assignment is designed to guide students through the research process and so it is also expected that feedback on the research approach and analytic work will be provided throughout the process. Students will also be encouraged to engage with generative AI chatbots to help refine model code for their analysis.

Module aims

  • Provide students with a clear description, definition of, and discussion of some of the most popular approaches to social data science
  • Enable students to critically evaluate the full range of quantitative research conducted in the social sciences
  • Provide students with hands on experience of data science approaches suitable for a range of situations including theory testing, prediction, and causal effects modelling using quasi-experimental design
  • Understand how new developments in generative AI can be leveraged to form a central part of the social data science workflow

Learning outcomes

Attributes Developed
001 Have a critical awareness of the rationale and terminology of social data science approaches K
002 Be able to engage with existing quantitative research, highlighting its key strengths and weaknesses KT
003 Have a comprehensive understanding of the logic of model development and theory testing CP
004 Be able to develop prediction models using machine learning algorithms and test their validity CP
005 Be able to generate plausible control and treatment groups for testing causal effects using quasi-experimental matching approaches CP

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:


Provide students with a working knowledge of data science techniques to deal with a range of different data scenarios. Alongside theoretical knowledge, workshops will give students practical experience of different techniques, and how they should be applied. Students will also learn how to critically assess their models.


The learning and teaching methods include:


  • Lectures

  • Practical workshops in R

  • Group discussion and feedback


This module is taught in weekly two-hour sessions. Each session will consist of a combination of lectures and hands on practical sessions with the statistics package R (

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: SOCM081

Other information

The Department of Sociology is committed to developing graduates with strengths in digital capabilities, employability, global and cultural capabilities, resourcefulness and resilience and sustainability. The MSc programme in Social Research specifically develops these strengths with a view to preparing graduates for careers in social research. This module aims to develop students’ grasp of a set of key skills and awareness to underpin their future research. In particular, it supports students to develop in the following key areas.


Digital capabilities. Use of appropriate software and access to digital resources are essential for the contemporary social researcher. This module supports students to develop skills and confidence in the use of the widely used software R and some of the key data science approaches.


Employability. The module aims to develop skills that are highly significant in the workplace for a social researcher, with a view to supporting students to be able effectively to use quantitative analysis to inform real world problems. Data science is a clear area of growth in the UK economy and students from this module will be well equipped to contribute in this area.


Resourcefulness and resilience. Students are encouraged to see themselves as engaged in a process of continual development of their skills and as needing to keep abreast of emerging developments. Data science techniques often go hand in hand with new developments in AI and students will be encouraged to integrate these tools (e.g. ChatGPT) into their programming workflow.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Social Research MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.