Spatial Analysis and Data Science
Spatial data science solves problems by transforming data into useful information. It enriches traditional data science by incorporating spatial characteristics such as proximity, coincidence, and connectivity in creating models and making predictions. Spatial data science gives students unique skills and advantages that are in high demand in the workplace. This web page is your first step in learning how to incorporate spatial data science into your curriculum through capabilities, examples, success stories, and training.
Combine powerful and out-of-the-box analytic tools with integration into external frameworks.
The value of teaching reproducibility
Dr. Tyler Davis, professor at the College of William & Mary, pioneers a multifaceted class in data reproducibility and problem solving.
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Request help from Esri’s education team to add spatial data science to your teaching and research.