See what others can’t with spatial analysis and data science (sponsored by Esri)
Data is everywhere, but now over 80% of data is spatial. Whether it’s a coordinate, address, place name, or postal code, location information is at the heart of digital transformation. Spatial data science allows organizations to combine information, find patterns, make predictions and build insight—helping them to see data, challenges, and the world differently.
Shannon Kalisky and Alberto Nieto explore how to combine industry-leading spatial analytics with modern data science frameworks to support your end-to-end analytical processes. As organizations navigate the new waves of innovation brought on by artificial intelligence, spatial problem solving will be key in accurately defining problems, finding answers, and building explainable workflows.
This session is sponsored by Esri
What you'll learn
- Learn how to harness spatial analytics
Shannon Kalisky is the lead product manager for analytics and data science at Esri, where she works with development and engineering teams to bring spatial data science mainstream. She started her career in geographic information system (GIS) where she worked for a variety of organizations ranging from government to Fortune 500 companies, leveraging spatial data to uncover patterns and build predictive models with a combination of GIS and Python. Her undergraduate studies were in geography and her graduate education was in community and regional planning. She’s pursuing her MBA in global business. When she’s not working behind a computer, you’re most likely to find Shannon with her hands dirty in a garden or at the local hardware store gathering supplies for her next project.
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