120 compulsory credits and 60 optional credits.
In addition to the 120 credits of compulsory modules, you must take 60 credits of optional modules from Sociology, Philosophy and Anthropology (SPA) and Politics. Suggested relevant modules are listed below, or you may take any postgraduate optional modules from the department of Social and Political Sciences, Philosophy, and Anthropology. View option modules for SPA and for Politics .
SPAM003: Computational Social Science 1
Technological advancements have not only driven the digitisation of society and the emergence of novel socio-political issues, but have also resulted in significant developments in algorithms, computational power, and increasingly large datasets. This practical-based module will provide you with both the technical programming skills and understanding of data science techniques that you will need to research pre-existing and novel social-political and economic issues. Specifically, it will introduce you to the Python programming language, assuming zero prior-experience, and give you the skills necessary to use it for data analysis.
This module has two main aims. The first is to introduce you to the Python programming language and to the fundamental concepts underlying programming in general. This includes, but is not limited to, variables, coding architecture, iteration operations, file input/output, data structures, plotting data, numerical and statistical techniques, and importable packages. The second aim of the module is to build upon the first and train you in how to use Python for data analysis.
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SPAM004: Computational Social Science 2
Technological advancements have not only driven the digitisation of society and the emergence of novel socio-political issues, but have also resulted in significant developments in algorithms, computational power, and increasingly large datasets. This practical-based module will provide you with both the knowledge and skills necessary to research pre-existing and novel social-political and economic issues. Specifically, it will build upon the content covered in the Computational Social Science 1 module in developing your skills and understanding in several contemporary computational research methods, such as natural language processing, computer simulation, and social network analysis, and other machine learning/artificial intelligence approaches.
The aim of this module is to build upon the content of the Computational Social Science 1 module, which introduced you to the Python programming language and to the fundamental concepts underlying programming in general and how to use Python for data analysis, by introducing you to several computational social science research methods that are currently frequently used to investigate various social-political and economic issues.
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SSIM907: Policy Analytics: Dissertation or Research Consultancy Project
In consultation with a supervisor, you will undertake an extended piece of original research related to policy analytics and/or evidence-based decision-making in a subject area related to your interests. If you choose to complete a dissertation based on a research consultancy project, this will involve a placement working with a non-academic partner. The decision of which route to take is left entirely up to the individual student (i.e., undertaking a placement is not compulsory). Each year we try to offer a number of pre-arranged placements, but students are welcome to reach out to other organisations for opportunities, with the guidance of the module convenor.
This module aims to:
- provide you with an opportunity to conduct independent research-based academic work in the area of policy analytics related to an area in which to pursue further research or your career
- develop your ability to apply originality and data analysis skills alongside theory and practice to a specific research topic. This may be consultancy- or workplace-based.
- enhance your awareness of the methodological, ethical and practical concerns of social science and data driven research.
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SSIM915: Statistical Modelling
Statistical models help us deal with the messy complexity of the social world. In this module, you will learn how to select and estimate models which are appropriate for social science data. Using these models is a core data science skill. Taking this course will help you to understand the overall framework of generalised linear models and to fit regression models suitable for continuous or categorical outcomes using the statistical software R. You will also understand better how to work with data. The course is suitable for students with some prior experience of quantitative methods.
The aims of this module are to enable students to be able to:
- Develop skills in working with social science data using R
- Estimate regression models for continuous or categorical outcomes
- Understand the appropriate model for the data
- Interpret and compare the outputs of statistical models
- Explain the findings from their analyses
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SSIM918: Data Visualisation
This course will introduce you to the field of data visualisation. You will learn basic principles of data and model visualisation. The focus will be on the principles of turning data into graphical representations for describing and exploring data, analysing hypotheses and relationships and presenting evidence. Particular attention will be paid to visualising data for policy audiences. You will learn techniques for visualising different types and formats of data utilising industry standard, open source software.
The main aims of the module are:
- To understand and apply principles of data visualisation
- To develop skills in capturing and managing data for visualisation
- To analyse subject relevant data sets using data visualisation techniques
- To learn to quantitatively and qualitatively evaluate existing visualisations
- To further develop skills in using the ggplot2 package for R and related packages for data visualisation.
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POLM063: Qualitative Methods in Social Research
This module engages with core issues in conducting qualitative research in social sciences and addresses the opportunities, challenges and prospects for designing sound qualitative research design. We engage with the logics of inquiry in qualitative research and their implications for validity and generalisability; we discuss what constitutes a case and what the role of context is; we identify the main types of data and what opportunities and limitations are for mixing methods and/or data: we address the core expectations regarding ethics, transparency and reproducibility and assess the opportunities and challenges in publishing qualitative research in social sciences. This module is designed for students who are engaging with empirical research in social sciences. You are expected to have familiarity with research methods in social sciences and to engage, over the course of this module, with one or several specific method(s) for qualitative research.
The aims of this module are to introduce you with core issues in qualitative research in social sciences; to enable you to gain a critical understanding of the opportunities, challenges and prospects for qualitative research in social sciences; and to provide you with the tools for designing a sound research design for qualitative inquiry in social research.
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POLM140: Qualitative Methods in Social Research
This module engages with core issues in conducting qualitative research in social sciences and addresses the opportunities, challenges and prospects for designing sound qualitative research design. We discuss the logics of inquiry in qualitative research and their implications for validity and generalisability; we consider what constitutes a case and what the role of context is; we identify the main types of data and what opportunities and limitations are for mixing methods and/or data; we address the core expectations regarding ethics, transparency and reproducibility and assess the opportunities and challenges in publishing qualitative research in social sciences. This module is designed for students who are engaging with empirical research in social sciences. You are expected to have familiarity with research methods in social sciences and to engage, over the course of this module, with one or several specific method(s) for qualitative research.
The aims of this module are to introduce you with core issues in qualitative research in social sciences; to enable you to gain a critical understanding of the opportunities, challenges and prospects for qualitative research in social sciences; and to provide you with the tools for designing a sound research design for qualitative inquiry in social research.
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SOCM033: Data Governance and Ethics
Data science, machine learning, artificial intelligence and 'big data' have become central to every aspect of social life. How can these complex and powerful technologies best be managed and governed for the benefit of society now and in the future? In this module you will: (1) identify some of the main risks and ethical/legal challenges involved in the widespread automation and digitalisation of services characterising 21st century life (for example, the clash between individual desire for privacy, frameworks for data ownership and the institutional commodification of personal data); (2) examine whether and how such concerns can be handled; and (3) discuss the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.
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SPAM002: Security, Artificial Intelligence and Emerging Technologies
Advancements in emerging technologies, including biotechnology, quantum computing, 3D printing, and particularly artificial intelligence (AI), pose new security concerns, such as aggravating social tensions and norms, creating new security vulnerabilities, and encouraging relative power shifts at both the inter-nation state and state versus sub-state actor levels. At the same time, these technologies have been employed in areas such as homeland security and crime prevention to protect individuals, assets, and sensitive data. This module will introduce you to AI and other emerging technologies, their impact on the security landscape, and provide you with the skills necessary to implement small-scale AI algorithms.
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SPAM008: Mapping the Social World: Introduction to Spatial Analysis in the Social Sciences
This module introduces you to thinking spatially in the social sciences by providing an overview of spatial themes and techniques, drawing examples from criminology, political science, sociology, and other disciplines. It covers introductory and intermediate concepts and tools related to Geographic Information Systems (GIS), using appropriate software. Emphasis is evenly split between learning how to make maps and a variety of spatial analyses. These spatial skills can be used in social science research, as well as in law enforcement, public policy, and other applications. Although familiarity with basic descriptive statistics is assumed, the module is hands-on and targeted to beginner students with an interest in maps, spatial analysis, or social geography. This module provides you with an applied introduction to spatial analysis using GIS in the social sciences. You will learn about spatial construction of place, basic mapping skills and spatial data creation and geoprocessing, and statistical methods to explore and model spatially referenced data using appropriate software. These spatial skills can be used in a variety of careers, including law enforcement, public policy analysis, and data-driven journalism.
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SPAM031: Social Networks
Social relationships critically constrain and enable our lives. In this module we look at how to measure, describe, and analyse a variety of different types of social networks from communication, support, and conflict. We will explore this relational perspective and how it can change our approach to social theories. We will also look at collecting social network data both in online and offline settings. We will also introduce methods to describe and analysis social networks. No previous experience with any social network software or coding experience is expected.
You will learn about the theories of social networks and how these ideas impact our understanding of other social science topics like political engagement, social capital, and deviance. We also discuss motivations for using social network analysis and the strengths and weaknesses of this approach in a variety of social science contexts. Using a combination of lectures, practical demonstrations and assignments, you will also gain experience in describing and visualising social networks.
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SSI3021: Mapping the Social World: Introduction to Spatial Analysis in the Social Sciences
This module introduces students to thinking spatially in the social sciences by providing an overview of spatial themes and techniques, drawing examples from criminology, political science, sociology, and other disciplines. It covers introductory and intermediate concepts and tools related to Geographic Information Systems (GIS), using appropriate software. Emphasis is evenly split between learning how to make maps and a variety of spatial analyses. These spatial skills can be used in social science research, as well as in law enforcement, public policy, and other applications. Although familiarity with basic descriptive statistics is assumed, the module is hands-on and targeted to beginner students with an interest in maps, spatial analysis, or social geography.
This module provides you with an applied introduction to spatial analysis using GIS in the social sciences. You will learn about spatial construction of place, basic mapping skills and spatial data creation and geoprocessing, and statistical methods to explore and model spatially referenced data using appropriate software. These spatial skills can be used in a variety of careers, including law enforcement, public policy analysis, and data-driven journalism.
View an example full module specification
