The key objective is to have graduates who are able to join data science teams in the Government, corporate, or private sectors, with sufficient understanding of technical concepts in data science and machine learning to collaborate with computer scientists and engineers and with sufficient understanding of social science and politics to be able to bring a deeper understanding of human behaviour to otherwise technology oriented teams.
Core modules for the Social Science Background stream provide a foundational understanding of data science methods, while optional modules allow exploration into specialised areas like machine learning, quantitative text analysis, and the ethical use of AI in politics. Similarly, the Technical Background stream includes core and elective modules that blend technical skills with an understanding of political science theories and applications.
Graduates can also use the skills acquired to continue work in political science research, either in academia, think tanks, or the non-profit or public sector, where they will benefit from a deep understanding of the cross-section between data science and political science.
Featured Modules
The MSc Politics and Data Science consists of a variety of modules designed to train learners to become experts in the latest quantitative methodologies and research skills. Below, we describe a selection of modules students can choose from. The full list of modules is available below under “Which modules can I take?” Please note that these are selected example modules and some are not guaranteed to run each year.
Introduction to Statistics: This module offers an overview of statistical analysis fundamentals in political science and related fields, focusing on measurement, variables, and statistical data handling. It introduces descriptive statistics, multiple regression analysis, and statistical inference, teaching students to draw conclusions from sample data. The course also covers practical R programming for data analysis, addressing linear regression assumptions, estimation, and inference. Key topics include data visualisation, regression models, hypothesis testing, and logistic regression. Upon completion, students will understand basic statistical concepts and R programming and be able to interpret regression analyses, equipping them for analytical tasks in social science research.
Applied Data Wrangling and Visualisation: This module provides a practical introduction to data management and visualisation techniques using R. Students will learn essential skills in data cleaning, wrangling, merging, and handling various file formats. The course also introduces AI tools for coding assistance, project management, and automation in data workflows. Beyond data preparation, the module covers the principles of effective data visualisation, guiding students through applied techniques for creating clear and engaging visual representations of data. It also includes an introduction to relational databases with SQL and web scraping for data collection, enabling students to work with large datasets efficiently. By the end of the module, students will have the skills to manage, analyse, and visualise data effectively, preparing them for data-driven roles in research, policy analysis, and other fields requiring strong data literacy.
Quantitative Text Analysis: This module equips students with the ability to analyse vast text corpora, employing both traditional statistical methods and cutting-edge machine learning techniques like transformer models and Large Language Models. Throughout this module, students will gain hands-on experience in the R and Python programming languages, learning to navigate the process from data extraction to analysis. The module combines established text-as-data methods and advanced methods, including transformer-based machine learning, word embeddings, and Large Language Models, preparing students to apply these methods to address critical social and political questions. By mastering these skills, students can to harness the full potential of automated text analysis in their future careers.
Programming for Social Scientists: This module is a foundational course in computer programming, focusing on Python, currently the third most popular programming language and a favourite among data scientists for its accessibility and versatility. It is designed to equip students, particularly those in the social sciences, with the skills to automate tasks and develop more complex software, in particular using object-oriented design patterns. The curriculum emphasises hands-on learning through creating a social simulation project in teams, allowing students to apply basic programming skills to various applications, including file manipulation, user interface design, simulation modelling, and result visualisation. Combining lectures with labs and homeworks, the module supports practice with Python and related tools, fostering collaboration and self-reflection.
AI and Language Models in Politics: This module on AI and large language models (LLMs) equips students with an in-depth understanding of cutting-edge language models’ theoretical foundations, development techniques, ethical considerations, and practical applications. The module aims to provide students with the knowledge and skills necessary to design, implement, and evaluate language models in political contexts, as well as to critically analyse the implications of deploying such models. Throughout the course, students will explore topics including the architecture of neural networks underlying LLMs, the effects of data collection and processing methods, model training and fine-tuning processes, and the evaluation of model performance and bias. Ethical considerations will be woven throughout the curriculum, addressing issues such as privacy, bias, fairness, and the societal impact of automated language generation. Students to gain hands-on experience with LLMs, preparing them for research, development, and policy-making roles where AI and language technologies are due to play an increasing role.
Connected_Politics: Under the guidance of both a project and a module coordinator, small teams will tackle a pressing social or political question using advanced methodologies such as quantitative text analysis, machine learning, image recognition, and network analysis. The focus is on developing teamwork skills, setting and achieving goals, and effectively distributing tasks within the group. Throughout the module, students will learn vital aspects of research design, substantive theory, formulating research questions, case-selection strategies, and the importance of open science practices. They will also explore the concepts of replicability and reproducibility in research. The results of projects from previous years have appeared in peer-reviewed journals, and groups have also presented their work at professional conferences such as the Annual Conference of the American Political Science Association.
Programme Outcomes
Knowledge and understanding
- Understanding the range of data science and machine learning methodologies that are available to data scientists, and their key advantages and disadvantages.
- Understanding of theories of political behaviour, political processes, and political institutions.
- Understanding variations in political systems and their functioning.
Applying knowledge and understanding
- Understanding of central aspects of political and social science research design, such as conceptualization, operationalization and measurement.
- Ability to use knowledge of research design to systematically address questions pertaining politics and public policy.
- Gain general experience in applying data science techniques to questions of political and social science relevance.
Making judgements
- Ability to decide on appropriate statistical techniques given a particular research question in relation to political behaviour and public policy.
- Ability to evaluate reported statistical and algorithmic results in political and social science research.
- Through training in general research design, ability to evaluate the veracity of input data of political and social behaviour for use in data science applications.
- Have a basic understanding of the situations where automated techniques as used in standard data science practice are suitable and ethically appropriate, and where not.
Communications and working skills
- Ability to clearly communicate results from statistical analysis of political and social behaviour.
- Ability to communicate the possibilities and scope of data science tools for the understanding of political and social behaviour.
- Basic practice in team work and learning how to collaborate in larger technical projects, including ability to work with techniques for code sharing, agile development, tools for scientific replication, etcetera.
Learning skills
- Have sufficient grounding in fundamentals of statistical analysis and computer science to be able to acquire new skills in data science.
- Have sufficient grounding in political and social science to be able to read into new domains of political and social science research.