Explore our Big Data and Digital Futures taught Master's degree.
Join Warwick's Big Data and Digital Futures MSc/PGDip and learn to critically engage with big data. The Centre for Interdisciplinary Methodologies works across disciplines, drawing from the Arts, Humanities, Social Sciences and Sciences, to answer employers' demands for a new generation of researchers.
This degree responds directly to the growing demand across research fields and by employers in society for a new generation of postgraduates who can critically engage with big data theoretically, methodologically and practically. In contrast to many big data-focused degrees (such as Data Science or Data Analytics) where the emphasis is almost exclusively on data practices and computational tools, this degree underpins key practical skills with a range of theoretical approaches to data.
How is our world influenced by big data? How are our lives represented in big data? This course will enable you, whatever your disciplinary background, to understand and act in a society transformed by data, networks and computation and develop a range of interdisciplinary capacities.
Our course offers you:
- Core knowledge in statistical modelling and programming for data-driven careers
- An extensive understanding of the relationship between big data technology and society
- Practical and critical application of these techniques to cutting-edge methods across the data spectrum
- R programming skills (using RStudio)
- Statistics in Social Science (up to multiple linear regression and logistic regression)
- Advanced Statistics (generalised linear models, multilevel modelling and casual inference)
- Basics in Social Network Analysis, Web Scraping, Reproducible Analysis, Data Visualisation, SQL, Deep Learning, Agent-Based Modelling (From Q-Step Masterclasses)
- Writing and communication skills for analysis/discussing technical content
- Critical academic research skills with an interdisciplinary focus
General entry requirements
2:i undergraduate degree (or equivalent).
English language requirements
You can find out more about our English language requirementsLink opens in a new window. This course requires the following:
- Band B
- IELTS overall score of 7.0, minimum component scores of two at 6.0/6.5 and the rest at 7.0 or above.
We welcome applications from students with other internationally recognised qualifications.
For more information, please visit the international entry requirements pageLink opens in a new window.
There are no additional entry requirements for this course.
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'?
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
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.
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.
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.
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 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
Modules in this course make use of a range of teaching and learning techniques, including, for example:
- Blended learning including the use of an online virtual learning environment
- Student group and project work
- Reading and directed critical discussion
- Independent research by students
- Practice-based activities
For this course a typical seminar size is around 16 students.
Typical contact hours
There are around 7-9 hours contact hours per week for this course, depending on optional modules chosen.
A combination of essays, reports, design projects, technical report writing, practice assessments, group work and presentations and an individual research project (10,000 word dissertation).
Most departments have reading lists available through Warwick Library. If you would like to view reading lists for the current cohort of students you can visit our Warwick Library web pageLink opens in a new window.
Your personalised timetable will be complete when you are registered for all modules, compulsory and optional, and you have been allocated to your lectures, seminars and other small group classes. Your compulsory modules will be registered for you and you will be able to choose your optional modules shortly before joining us.
The MSc will enable students from various disciplinary backgrounds to understand and act in a society transformed by big data, networks and computation and develop a range of interdisciplinary capacities.
Upon graduation, students can follow an academic or professional career working in knowledge-based companies, NGOs, and in such fields as information policy, new media production, public relations, or administration and entrepreneurship in big data and digital culture-based companies.
The MSc can also lead to academic studies e.g. the MPhil/PhD in Interdisciplinary Studies or other relevant disciplines. Likewise, the Postgraduate Diploma and Postgraduate Certificate allow excellent foundations for students from different disciplinary backgrounds to learn more about big data and to skill-up in a relatively short time on particular research skills.
Our department has a dedicated professionally qualified Senior Careers Consultant offering impartial advice and guidance together with workshops and events throughout the year. Previous examples of workshops and events include:
- Warwick careers fairs throughout the year
- Careers in AI and Data Science
- Discovering Careers in the Creative Industries
- Discuss What’s Next After Your CIM Master’s Degree
Centre for Interdisciplinary Methodologies (CIM)
The Centre for Interdisciplinary Methodologies (CIM) was established at Warwick in 2012 to foster innovative and experimental forms of knowledge production through a sustained focus on methodology. CIM is dedicated to expanding the role of interdisciplinary methods through new lines of inquiry that cut across disciplinary boundaries, both intellectually and institutionally.
Method is central to the formation and transformation of disciplinary knowledges, and the challenge of working across and in between disciplines is both exciting and pressing. Our research team is drawn from across the Arts, Humanities, Social Sciences and Sciences, with expertise in a variety of substantive domains.
Within Warwick, CIM is an advocate of interdisciplinary research and study. Beyond Warwick and beyond the academy, CIM explores new forms of public engagement, both with potential research users and with the experts, experimenters and institutions in business, civil society and government that are at the forefront of applied methodological innovation.
Our Postgraduate courses
Tuition fees are payable for each year of your course at the start of the academic year, or at the start of your course, if later. Academic fees cover the cost of tuition, examinations and registration and some student amenities.
Fee Status Guidance
The University carries out an initial fee status assessment based on information provided in the application and according to the guidance published by UKCISA. Students are classified as either Home or Overseas Fee status and this can determine the tuition fee and eligibility of certain scholarships and financial support.
If you receive an offer, your fee status will be stated with the tuition fee information, however we are awaiting guidance from the UK government regarding fee status for EU, other EEA and Swiss nationals and their family members living in the UK for academic year 2021/22 onwards. We are not able to confirm the fee status for these students until the relevant eligibility criteria have been confirmed. Once we have received further information from the UK government, we will provide you with an update on your fee status and let you know if any additional information is required. If you believe your fee status has been incorrectly classified you can complete a fee status assessment questionnaire (follow the instructions in your offer) and provide the required documentation for this to be reassessed.
The UK Council for International Student Affairs (UKCISA) provides guidance to UK universities on fees status criteria, you can find the latest guidance on the impact of Brexit on fees and student support on the UKCISA website.
Additional course costs
Please contact your academic department for information about department specific costs, which should be considered in conjunction with the more general costs below, such as:
- Core text books
- Printer credits
- Dissertation binding
- Robe hire for your degree ceremony
Scholarships and bursaries
Find out how to apply to us, ask your questions, and find out more.
Here is our checklist on how to apply for taught postgraduate courses at Warwick.
Here is our checklist on how to apply for research postgraduate degrees at the University of Warwick.