A primary degree with at least Second Class Honours Grade 1 (2H1). 2H1 is equivalent to 60 per cent, B minus or 3.08 GPA - in American system: B or 3.00 GPA.
Your application will be considered on its individual merits and relevant professional experience will also be taken into account.
English language requirements: applicants whose first language is not English should have met TOEFL, IELTs, or computer-based TOEFL requirements (600, 6.5, or 250 respectively), or the Cambridge English Test (Certificate in Advanced English at a minimum of Grade B, or Certificate of Proficiency in English at Grade C). Applicants who obtained a previous degree from an English-speaking university may be exempted from this requirement.
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These are the minimum entry requirements – additional criteria may be requested for some programmes
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. 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. : 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 un 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 : This module equips students with the ability to analyse vast text corpora, employing both traditional statistical methods and cutting-edge machine learning techniques like trans