Environmental & Climate Science
Apply computational methods to understand environmental systems, climate patterns, hydrology, and ecological dynamics.
Environmental and Climate Science sits at the intersection of earth systems knowledge and computational analysis. Modern environmental scientists use Python, GIS, remote sensing, machine learning, and statistical modelling to monitor ecosystems, model climate behaviour, analyse hydrological systems, and communicate scientific findings to policy makers and the public. Research Atlas provides structured pathways through the computational tools that define modern environmental data science — from foundational Python programming to advanced geospatial analysis, hydrological modelling, and machine learning applications in climate and ecology. The flagship Bluewater Basin research environment gives you a realistic scientific scenario: a fictional catchment with terrain data, well records, land cover, and rainfall — everything you need to practise the complete computational workflow from raw data to scientific finding.
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.
Computational Hydrologist
Models water movement, groundwater behaviour, and watershed dynamics using numerical methods, GIS, and statistical analysis.
GIS Analyst
Creates, analyses, and manages geospatial data to support scientific, environmental, and planning decisions.
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.
Agritech Data Scientist
Applies remote sensing, GIS, and machine learning to precision agriculture and food systems.
Research labs
Learning resources
Python for Environmental Data
Learn Python programming through environmental data problems — loading, cleaning, and analysing environmental datasets.
GIS for Environmental Science
Geographic Information Systems methods for environmental analysis — spatial data, mapping, and terrain analysis.
Statistics for Environmental Science
Applied statistical methods for environmental data — hypothesis testing, regression, and spatial statistics.
Remote Sensing for Environmental Monitoring
Use satellite and aerial imagery to monitor environmental systems and detect change.
Machine Learning for Environmental Science
Apply machine learning to environmental prediction — classification, regression, and model evaluation.