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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
GISC 4381 Rod Sardari | |||||
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Not teaching in Spring 2026 | |||||
GISC 4381 Rod Sardari | |||||
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This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
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Spatial Data Science
GISC 4381
School of Economic, Political and Policy Sciences
Data science has emerged as one of the key drivers of societal transformation. Many data have intrinsic spatial properties (e.g., locations, geometry, and boundary) and subsequent relationships and interactions in space and time. Such spatial data are critical to find solutions or develop applications for emergency management, environmental sustainability, public health, smart city, public safety, business logistics, driverless cars, ecological conservation, and many other problem domains. This course aims to help students develop knowledge and skills to ask spatial questions, find, process and analyze spatial data, solve spatial problems, and communicate their findings. Specifically, the course will introduce spatial data analytics and machine learning methods. Central to the course is learning how to think through spatial problems and formulate solutions in spatial data science frameworks. How can we leverage spatial properties of data to advance our understanding of the roles, functions, and processes of location, space, and place on the society, environment, and their interactions? How does spatiality provide efficient frameworks to organize information, conceptualize real-world problems, and innovate solutions? Technically, we will explore machine learning with ArcGIS and proceed with open-source Python libraries (e.g., pandas, geopandas, scipy, stats, scikitlearn, and if time permits, keras and tensorflow) with cloud technologies, for example, AWS, Microsoft Azure, and Google Cloud Platform. 3 credit hours.
Offering Frequency: Each year
Grades: 64
Median GPA: B+
Mean GPA: 3.159
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This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Spatial Data Science
GISC 4381
School of Economic, Political and Policy Sciences
Data science has emerged as one of the key drivers of societal transformation. Many data have intrinsic spatial properties (e.g., locations, geometry, and boundary) and subsequent relationships and interactions in space and time. Such spatial data are critical to find solutions or develop applications for emergency management, environmental sustainability, public health, smart city, public safety, business logistics, driverless cars, ecological conservation, and many other problem domains. This course aims to help students develop knowledge and skills to ask spatial questions, find, process and analyze spatial data, solve spatial problems, and communicate their findings. Specifically, the course will introduce spatial data analytics and machine learning methods. Central to the course is learning how to think through spatial problems and formulate solutions in spatial data science frameworks. How can we leverage spatial properties of data to advance our understanding of the roles, functions, and processes of location, space, and place on the society, environment, and their interactions? How does spatiality provide efficient frameworks to organize information, conceptualize real-world problems, and innovate solutions? Technically, we will explore machine learning with ArcGIS and proceed with open-source Python libraries (e.g., pandas, geopandas, scipy, stats, scikitlearn, and if time permits, keras and tensorflow) with cloud technologies, for example, AWS, Microsoft Azure, and Google Cloud Platform. 3 credit hours.
Offering Frequency: Each year
Grades: 64
Median GPA: B+
Mean GPA: 3.159
Click a checkbox to add something to compare.