Course summary

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 Social Sciences, Computing Sciences, and Arts & Humanities 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, cloud computing, and contemporary artificial intelligence (AI) 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 and AI? How are our lives represented in different formations and transformations of 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 programming and statistical modelling 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 Jupyter/IPython and RStudio)

• Statistics for the Social Sciences (up to multiple linear regression and logistic regression)

• Advanced Statistics (generalised linear models, multilevel modelling and casual inference)

• Data Science (including theory, computational methods, and conceptual critique)

• Artificial Intelligence (from machine learning and neural networks to Generative AI)

• Cloud Computing (concepts and practical applications using Microsoft Azure)

• 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