Graduate Certificate in Spatial Data Science

Spatial Data Science is an emerging field that utilizes data science techniques to analyze spatial data about the environment, society, and economy on the Earth surface. Massive volume of spatial data in diverse formats are being constantly collected by various platforms using different sensors, demanding for not only latest data sciences technologies to explore, process and analyze the big spatial data, but also specialized methods to visualize, detect patterns and discover knowledge from spatial data due to their unique and complex characteristics of (i.e., locational information).

Spatial Data Science certificate is an interdisciplinary program that integrates the strengths of Geospatial Information Sciences and Social Data Analytics and Research Programs. It focuses on offering geospatial professionals with data collection and analytical, knowledge mining, machine learning, big data handling, and programming skills, and equipping data science professionals with capabilities in geographic information systems (GIS), remote sensing/drone, geovisualization, locational intelligence, and spatial pattern analysis, which allows the solving of real world location problems in various areas including agriculture, environment management, insurance, marketing, retail and real estate, oil and mining, public health, social and public services, transportation, smart city and urban planning.

Admission Requirements

Students seeking the Graduate Certificate in Spatial Data Science should submit an application with their undergraduate transcript GPA of at least 3.00; a Current transcript with a GPA of at least 3.00 for the embedded certificate program; and at least two letters of recommendation for the stand-alone certificate program.

Fact Sheet (pdf)

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