Half length modules equivalent to roughly 24 to 30 student engagement hours within a week

For more information please contact runck014@umn.edu

Courses

GEMS X001.1 - Computing Basics for the Agri-food Sector: Introducing the GEMS platform + JupyterLab
GEMS X001.2 - Computing Basics for the Agri-food Sector: Demystifying the UNIX command line
GEMS X001.3 - Computing Basics for the Agri-food Sector: Working Remotely and scheduling jobs on MSI’s systems
GEMS X003.0 - Accounting for Location in Agriculture: An introduction to Spatial Data Analysis in R
GEMS X003.4 - Accounting for Location in Agriculture: Spatial regression in R
GEMS X003.5 - Accounting for Location in Agriculture: Geostatistics and Interpolation in R
GEMS X006.10 - Spatio-Temporal Accounting of Biotic Threats: SDM predictions, mapping, evaluation & interpretation
GEMS X006.11 - Spatio-Temporal Accounting of Biotic Threats: Introduction to SDMS & Data Preparation for Modelling
GEMS X006.12 - Species distribution models (SDMs): parametrizing, modelling, evaluation & interpretation
GEMS X006.6 - Spatio-Temporal Accounting of Biotic Threats: Introduction to species distribution modelling (SDM), concept, rationale, review
GEMS X006.7 - Spatio-Temporal Accounting of Biotic Threats: Sourcing & curating species occurrence datasets for SDMs
GEMS X006.8 - Spatio-Temporal Accounting of Biotic Threats: Environmental data, variable selection & processing for SDMs
GEMS X006.9 - Spatio-Temporal Accounting of Biotic Threats: parametrizing, modeling, testing
GEMS X007.1 - Title: Digital Agriculture: Getting Started Using Data to Support Decisions
GEMS X007.2 - Digital Agriculture: Using Simple Models to Guide Decision-Making
GEMS X008.1 - Explicitly Accounting for Location in Agriculture: Introduction to spatial data analysis in Python
GEMS X008.2 - Explicitly Accounting for Location in Agriculture: Spatial modeling in Python
Required fields are indicated by .