Dun & Bradstreet has announced a partnership with the United States Geospatial Intelligence Foundation (USGIF) to fund an academic scholarship to advance geospatial data science understanding and capabilities. The 2019 Dun & Bradstreet Geospatial Data Science Scholarship will award $15,000 to a graduate student pursing a Master of Science or Ph.D. in data science who is focused on solving data-intensive, large-scale, location-based problems using engineering, computer science, math, and/or spatial science.

“Dun & Bradstreet is pleased to work with USGIF to support emerging talent in a field that is crucial in the data science landscape,” said Tim Solms, Dun & Bradstreet’s General Manager, Government Services. “This scholarship will help the geospatial industry grow as it works to address and solve important national security challenges.”

The recipient of the Dun & Bradstreet Geospatial Data Science Scholarship must be enrolled in any full-time graduate program and be in good academic standing. His or her expertise should include geospatial data accessibility, spatial decision support systems, and geospatial problem-solving. The student’s academic pursuits should demonstrate understanding of how artificial intelligence, machine learning, and data mining can be used to augment geographic information science workflows to mine data and provide solutions.

“Today’s data scientists are expected to have a diverse skill set that includes mathematics, statistics, computer programming, and analysis as well as knowledge of social science,” said USGIF VP of Academic Affairs Dr. Camelia Kantor. “The geospatial component in this new data science scholarship highlights the important dimensions of location and time. Given that huge volumes of data have a location and time component, society needs people who can understand, use, and apply geospatial principles and tools. USGIF is thrilled to partner with Dun & Bradstreet in this effort.”

The winner of the scholarship, which will be administered by USGIF, will be announced in July.

Source: Geospatialworld.net