The virtual course "Spatial Data Science and Applications - Virtual Course - Coursera" is a course with different contents and offers video classes of Approx. 12 hours to complete. Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
Spatial (map) is considered as a core infrastructure of the modern IT world, which is supported by business transactions from major IT companies like Apple, Google, Microsoft, Amazon, Intel and Uber, and even car companies like Audi, BMW , and mercedes. Consequently, they are forced to hire more and more space data scientists. Based on such a business trend, this course is designed to introduce a firm understanding of spatial data science to students, who would have a basic understanding of data science and data analytics, and eventually to make their experience stand out. from other nominal data scientists and data. analysts In addition, this course could make students realize the value of big spatial data and the power of open source software to address spatial data science problems. This course will begin by defining spatial data science and answering why spatial is special from three different perspectives: business, technology, and data in the first week. In the second week, four disciplines related to spatial data science are presented together: GIS, DBMS, Data Analytics, and Big Data Systems, and related open source software: QGIS, PostgreSQL, PostGIS, R, and Hadoop. During the third, fourth and fifth week, you will learn the four disciplines one by one from the beginning to the applications. In the final week, five real-world problems and corresponding solutions are presented with step-by-step procedures in an open source software environment. Four disciplines related to spatial data science are presented together: GIS, DBMS, Data Analytics, and Big Data Systems, and related open source software: QGIS, PostgreSQL, PostGIS, R, and Hadoop. During the third, fourth and fifth week, you will learn the four disciplines one by one from the beginning to the applications. In the final week, five real-world problems and corresponding solutions are presented with step-by-step procedures in an open source software environment. Four disciplines related to spatial data science are presented together: GIS, DBMS, Data Analytics, and Big Data Systems, and related open source software: QGIS, PostgreSQL, PostGIS, R, and Hadoop. During the third, fourth and fifth week, you will learn the four disciplines one by one from the beginning to the applications. In the final week, five real-world problems and corresponding solutions are presented with step-by-step procedures in an open source software environment.
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