GEOS 4084 - Geospatial Data Analysis

4(2-4)

Prerequisite(s): MATH 1233 or MATH 1534 or MATH 1634 or permission of the instructor.

This course will introduce students to data analysis and visualization through the Python programming language and related skills. Students will learn how to install python and download the various packages, as well as how to download the remote sensing, climate, and modeling datasets that will be analyzed. Lectures will initially cover a broad range of topics related to reviewing and fundamental concepts in Python (e.g. data types, data structures, indexing, sub-setting, looping over data) useful for handling large volumes of data. Following that, the students will cover data handling topics: reading and manipulating large, multi-layer spatio-temporal datasets, vector and raster data manipulation, map reprojection, area selection based on geographic coordinates, masking data, and data visualization. Course work covers data analyses techniques on large geospatial datasets, including: applying basic statistics to large datasets, interpolation and smoothing, regression, function fitting for extracting information from time series data, change detection, image classification and spatial clustering, and dimensionality reduction. Throughout, the course will focus on developing the most fundamental and useful data science skills, such as cleaning and tidying raw data in preparation for analysis, and elegant and efficient code writing.

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