Admission into the Data Science graduate program
Proficiency with R and/or Python
GES 773: GIS Modeling Techniques
This course addresses the concepts, tools, and techniques of GIS modeling, and presents modeling concepts and theory as well as provides opportunities for hands-on model design, construction, and application. The focus is given to model calibration and validation.
GES 774: Spatial Statistics
This course investigates statistical techniques for exploring and characterizing spatial phenomena. The course covers local/global cluster analysis, spatial autocorrelation, interpolation, kriging, as well as exposure to prominent GIS statistical packages. An emphasis is placed on exploratory spatial data analysis (ESDA) to develop spatial cognition and analytical skills with practical applications to modeling spatial phenomena in computer environments.
GES 778: Advanced Visualization and Presentation
GES 770: Special Topics in Enterprise GIS
This course is to introduce the concept of geoprocessing and how to use Python to automate the process. Students will learn the basic syntax of Python, available system tools in ArcToolbox, model and ModelBuilder. They will also learn how to build a model and how to create a model tool. The core of the class is ArcPy site package. Students will work with the embedded ArcGIS Python window to write and execute scripts. They will also use the more advanced IDE tool Eclipse with PyDev plug-in. Students will write scripts using tools from ArcPy to perform various tasks, such as managing maps and layers, performing data editing, executing geoprocessing tools and creating custom geoprocessing tools. They will develop Python scripts in Eclipse. Once the Python scripts are developed, the student will learn how to convert it to a script tool so it will be available for other Geoprocessing models. In the end of the class, the students will be able to implement a geoprocessing solution for a complex geospatial problem.
GES 775: Advanced GIS Application Development
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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