Data Science for Ecology and Epidemiology University of Glasgow

Overview

The MSc Data Science for Ecology and Epidemiology is an applied programme designed to turn life scientists into quantitative data scientists with the rigorous technical expertise that leading research institutions and conservation organisations highly value.

Why this programme?

• The biological sciences are undergoing a data revolution. The challenge is no longer collecting data but the ability to analyse and interpret it. This programme exists to bridge that gap, transforming you into a scientist capable of turning complex biological data into robust evidence.
• Join a diverse postgraduate community where your quantitative focus intersects with peers in MSc Conservation Management of African Ecosystems and MSc Animal Welfare Science, mirroring the interdisciplinary teams found in professional research and government agencies.
• You will study within the School of Biodiversity, One Health and Veterinary Medicine, surrounded by active researchers working on everything from antimicrobial resistance, disease ecology, and wild immunology to African ecosystem management. This unique environment allows you to apply your quantitative skills to a vast array of real-world biological problems.
• Within the School’s diverse portfolio of masters degrees, this programme serves as the dedicated "quantitative hub". While other programmes focus on policy, welfare, or management, you will focus on the rigorous analysis and modelling that underpins them all. You will graduate with the elite technical skills required to support and influence the work of field biologists and policy-makers.
• We focus on understanding the processes that generate biological data and modelling them effectively. You will gain expertise in Generalised Linear Mixed Models (GLMMs), Machine Learning, Bayesian inference, and Deterministic and Probabilistic Causal frameworks — tools essential for cutting-edge research but rarely taught at this level in generalist biology degrees. While the taught components are delivered in R, we recognise the value of polyglot data science. Depending on your project scope and supervisor, there are opportunities to utilise Python or Julia, particularly for machine learning and computationally intensive modelling.