Big Data and Digital Futures (MSc/PGDip) (2022 Entry)
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
2:i undergraduate degree.
English Language requirements header
- 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.
International requirements header
Additional requirements header
There are no additional entry requirements for this course.
Fundamentals in Quantitative Research Methods
This module aims to provide an understanding of, and skills in applying, quantitative social research methods, consistent with the expectations of the ESRC in relation to core research methods training within the DTP partnership.
Advanced Quantitative Research
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 Masters 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 Masters 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 module header
- Interdisciplinary Approaches to Machine Learning
- User Interface Cultures: Design, Method and Critique
- Visualisation
- Digital Cities
- Digital Sociology
- Complexity in the Social Sciences
- Urban Resilience, Disasters and Data
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
Class size header
For this course a typical seminar size is around 12-16 students.
Contact hours header
There are around 7-9 hours contact hours per week for this course, depending on optional modules chosen.
Assessment header
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).
- 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
Reading lists
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 page.
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 when you join us.