💻 Software & Environment Setup
Environmental Science & Sustainability
The computational toolchain and environment used throughout Master Reinforcement Learning for Climate Modeling.
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# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install xarray rasterio geopandas earthengine-apiGoogle Earth Engine
Planetary-scale geospatial analysis
Planetary-scale geospatial analysis
QGIS
Open-source GIS & mapping
Open-source GIS & mapping
xarray / rasterio
Gridded climate & raster data
Gridded climate & raster data
OpenLCA
Life-cycle assessment modelling
Life-cycle assessment modelling
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in Master Reinforcement Learning for Climate Modeling
e-Certificate and e-Marksheet issued on successful completion.
