Data Science
Master the complete data science workflow — from data wrangling to machine learning, visualisation, and scientific communication.
Data science as applied to scientific research combines programming, statistics, machine learning, and domain knowledge to extract insight from structured and unstructured data.
Computational skills
Career paths
Environmental Data Scientist
Applies data science, GIS, and statistical methods to environmental problems — monitoring ecosystems, modelling climate impacts, and informing environmental policy.
Geospatial Data Scientist
Applies machine learning and advanced analytics to geospatial data at scale.
Climate Data Scientist
Analyses climate datasets, models future scenarios, and communicates climate risk.
Scientific Data Scientist
Applies data science methods across scientific disciplines to extract insight from experimental and observational data.
Research Software Engineer
Builds research software — scientific libraries, pipelines, and tools used by research communities.
Research Data Analyst
Supports research teams with data analysis, statistical methods, and reproducible workflows.
Scientific Machine Learning Engineer
Builds machine learning systems for scientific applications — from research prototypes to production scientific software.
Research labs
Labs for this domain are coming soon. Environmental Science has the first live labs.
Learning resources
Python for Environmental Data
Learn Python programming through environmental data problems — loading, cleaning, and analysing environmental datasets.
Statistics for Environmental Science
Applied statistical methods for environmental data — hypothesis testing, regression, and spatial statistics.
Machine Learning for Environmental Science
Apply machine learning to environmental prediction — classification, regression, and model evaluation.