Data Science

Spatial Analytics Pathway

The Spatial Analytics pathway focuses on data analytics techniques dealing with spatial (location) data. Spatial data are increasingly relevant to the goals of many organizations, yet spatial data have unique dimensions that require specialized data models, visualization, and analytic procedures. There is increasing demand for spatially skilled data scientists to exploit the ever-expanding sources of spatial and location-based data.

The courses in this pathway focus on geographic and spatial data, and the robust spatial data management, analysis, and visualization capabilities present in common data science platforms (R and Python). Many analytic capabilities only previously present in Geographic Information Systems (GIS) software have now been migrated to these open source environments where the value of spatial data integrated with non-spatial data sources can be most effectively realized. The pathway is a partnership between UMBC’s Department of Geography and Environmental Science and Department of Computer Science and Electrical Engineering.

Prerequisites

Admission into the Data Science graduate program
Proficiency with R and/or Python

Courses

Select three courses to fulfill the pathway requirement.

Career Outlook


Spatial Analytics is an emerging field with high job growth. While a subset of the broader discipline of Data Science and Analytics, leveraging location information is fundamental to many corporations (e.g. Uber, UPS, Walmart). Mobile devices, UAV-based sensing and delivery services, and any Location Based Service (LBS) is dependent on spatial data and analytics. According to Labor Insight, an employer-demand tool, employers in the Washington DC and Baltimore metropolitan areas seeking employees with skills in spatial analytics include Booz Allen Hamilton, Vencore, Leidos, and the National Geospatial-Intelligence Agency (NGA).

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