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Big Data and Digital Futures MSc/PGDip

Postgraduate Taught

Course code

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

Start date

27 September 2027

Study location

University of Warwick

Qualification

Master of Science (MSc)

Postgraduate Diploma (PGDip)

Department

Centre for Interdisciplinary Methodologies

Duration

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

Course overview

Develop a deeper understanding of big data.

Many big data degrees focus on technical skills, but our interdisciplinary course goes further - combining practical expertise with advanced theoretical perspectives. As well as understanding how data is collected and analysed, you’ll explore its social and ethical impact. Designed for students from a wide range of academic backgrounds, we’ll equip you with a blend of technical skills and theoretical understanding highly in-demand across industry, government, non-profit organisations and research.

Students having a friendly discussion indoors

Modules

Develop the theoretical and practical skills for a world transformed by data.

  • Social understanding: explore the relationship between big data and society.
  • Technical skills: understand statistical modelling and programming with Python and R, using infrastructure such as Jupyter Notebooks and Posit.
  • Machine learning: learn introductory data science and machine learning/AI techniques, including Generative AI.
  • Statistics: study Statistics in Social Science and Advanced Statistics.
  • Core skills: become proficient in Social Network Analysis, Web Scraping, Reproducible Analysis, Data Visualisation, SQL, Deep Learning and Agent-Based Modelling.

Note that information found by following the links to the module catalogue is subject to change for future years of study, as we evolve our courses in response to the latest developments in academia and industry.

Term 1

Term 2

You will choose one module from:

Term 3

Dissertation


CIM optional modules vary from year to year, and students choose a combination worth 60 CATs (credits). The links below are to the 20-CAT (credit) versions, but most are available at 15 and 30 credits as well. The 10 CATs are in addition to the 180 which need to be taken for a Master's degree. Example optional modules may include:

Teaching and learning

Combine practical training with critical understanding of big data.

  • Hands-on learning: gain experience with industry-relevant tools and technologies.
  • Interdisciplinary approach: draw insights and perspectives from the Arts, Humanities, Social Sciences and Sciences.
  • Critical thinking: evaluate the opportunities, limitations and societal impacts of data.
  • Communication skills: learn how to present and discuss technical content.

Entry requirements

Fees and scholarships

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 assessment and some student amenities.

Find your course fees

About the department

Careers

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How to apply

Our how to apply checklist helps you prepare for your application.

How to apply checklist

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