Core modules
Big Data Research: Hype or Revolution?

Big data is said to be transforming science and social science. In this module, you will critically engage with this claim and explore the ways in which the rapid rise of big data impacts on research processes and practices in a growing range of disciplinary areas and fields of study.

In particular, the module considers the following questions: What is big data? To what extent is 'big data' different to other kinds of data? What key issues are raised by big data? To what extent is big data transforming research practices? How are the 'nuts and bolts' of research practice (e.g. ethics, sampling, method, analysis, etc.) transformed with big data? To what extent are core concepts relating to research practice - such as comparison, description, explanation and prediction - transformed? To what extent can we critically engage with big data? How is big data transforming the 'discipline'?

Dissertation

The CIM Master’s dissertation is a piece of work (10,000 words) which addresses a single student-selected subject. The topic may concern any aspect of the subject matter of their Master’s programme. The dissertation is an exercise in independent study in which you can pursue a topic of interest. It allows you to further develop a range of independent research skills, including literature search and bibliography construction, theoretical argument, and generation/appraisal of empirical evidence.

Optional Core Modules
Term One
Data Science Across Disciplines

This module introduces students to the fundamental techniques, concepts and contemporary discussions across the broad field of data science. With data and data related artefacts becoming ubiquitous in all aspects of social life, data science gains access to new sources of data, is taken up across an expanding range of research fields and disciplines, and increasingly engages with societal challenges. The module provides an advanced introduction to the theoretical and scientific frameworks of data science, and to the fundamental techniques for working with data using appropriate procedures, algorithms and visualisation. Students learn how to critically approach data and data-driven artefacts, and engage with and critically reflect on contemporary discussions around the practice of data science, its compatibility with different analytics frameworks and disciplinary, and its relation to on-going digital transformations of society. As well as lectures discussing the theoretical, scientific and ethical frameworks of data science, the module features coding labs and workshops that expose students to the practice of working effectively with data, algorithms, and analytical techniques.

Or

Fundamentals in Quantitative Research Methods 

This module has two aims: introduce to academic quantitative literature, secondary data acquisition and management, and the use of applied statistics in the social sciences; prepare to attend further statistical training (including PO92Q: Advanced Quantitative Research) and make use of statistics in future research works, such as master's or PhD dissertations.

This will not be an abstract statistics module, but a comprehensive approach to social and political numbers, in keeping with all method modules students may have attended previously and concurrently. It will include example data from diverse fields of social sciences, in particular surveys on attitudes and opinions. Exercises and essays will be based on a selection of datasets, such as the British Social Attitudes Survey, the European Social Survey, the World Values Survey, and the International Social Survey Programme.

Term Two
Scaling Data and Societies

Big data technologies involve scaling-up — scaling up quantities of data, scaling up data infrastructures, scaling up data management, and scaling up the number of participants in a given technological system. This module provides an understanding of the technical. methodological and conceptual changes in the new forms of thinking, research and engineering required for understanding and working with scalable socio-technical systems. Beginning with the question of what 'scale' is in general and how data-based transformations redefine the limits of scale, the module presents students with a series of different ‘lenses’ through which the impact of scale manifests itself differently across contemporary data spaces, including hands-on laboratory exercises. By the end of the module, students will have gained knowledge and a greater appreciation of the impact of big data on research in socio-technical systems at various scales and, conversely, the multiple ways in which the concept of scale is driving developments in big data.

Or

Advanced Quantitative Research

This module introduces students to a selected set of advanced statistical methods that are commonly used in quantitative social research. You will cover three advanced methods such as regression diagnostics and interactions, logistic and multinomial regression modelling, multilevel modelling, cluster analysis and factor analysis. These methods allow you to answer questions such as: Why do some people support a given public policy (e.g. the death penalty, Brexit or the GAFA tax), and others not? What are the main nuances and cleavages within a party (e.g. the Greens) or an ideological orientation (e.g. the populists)?

To gain hands-on experience with answering these questions with social and political science data of varying complexity, you will apply these methods to existing small- and large-scale data sets. The expectation is that by the end of the module you will understand the basic principles of the advanced statistical methods covered, appreciate the context in which the methods are best applied, and have had practical experience of applying these methods to real-world data.

Optional modules
Optional modules can vary from year to year. Example optional modules may include:

Interdisciplinary Approaches to Machine Learning
User Interface Cultures: Design, Method and Critique
Visualisation Foundations
Digital Sociology
Complexity in the Social Sciences
Digital Objects, Digital Methods
Data Visualisation in Science, Culture and Public Policy