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Learner Reviews & Feedback for Spatial Data Science and Applications by Yonsei University

4.4
stars
451 ratings

About the Course

Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists. Based on such business trend, this course is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems. This course will start with 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 - GIS, DBMS, Data Analytics, and Big Data Systems, and the related open source software's - QGIS, PostgreSQL, PostGIS, R, and Hadoop tools are introduced together. During the third, fourth, and fifth weeks, you will learn the four disciplines one by one from the principle to applications. In the final week, five real world problems and the corresponding solutions are presented with step-by-step procedures in environment of open source software's....

Top reviews

MW

Aug 13, 2018

Great course. It helps I have a background in both Data Science and Geographic Information Science, but still found it equally interesting and challenging! I would highly recommend this course.

IT

May 19, 2022

Very grateful to Yonsei University for the course received, and I congratulate them for the effort made in preparing the course, highly recommended for beginners in Spatial Data Science.

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