Big Data and Digital Futures MSc/PGDip

Explore our Big Data and Digital Futures taught Master's degree at Warwick

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P-L990 (MSc); P-L991 (PGDip)

MSc/PGDip

MSc: 1 year full-time, 2 years part-time; PGDip: 9 months full-time, 21 months part-time

29 September 2025

Centre for Interdisciplinary Methodologies

University of Warwick

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
• Python and R programming skills (using RStudio)
• Introductory Data Science and Machine Learning / AI techniques, including Generative AI
• 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
• Writing and communication skills for analysis/discussing technical content
• Critical academic research skills with an interdisciplinary focus

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
• Lectures
• Seminars
• Reading and directed critical discussion
• Independent research by students
• Practice-based activities

For this course, a typical workshop contains around 20-30 students, and a typical seminar around 16 students.

There are around 8-10 hours contact hours per week for this course, depending on type and number of 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).

Reading lists 

If you would like to view reading lists for current or previous cohorts of students, most departments have reading lists available through Warwick Library on the Talis Aspire platformLink opens in a new window. 

You can search for reading lists by module title, code or convenor. Please see the modules tab of this page or the module catalogueLink opens in a new window.  

Please note that some reading lists may have restricted access or be unavailable at certain times of year due to not yet being published. If you cannot access the reading list for a particular module, please check again later or contact the module’s host department.  

Your timetable

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.

2:1 undergraduate degree (or equivalent).

• 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.

There are no additional entry requirements for this course.

Optional Core Modules

Term One

Term Two

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