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.

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Solution Structures of Spatial Data Science Problems

The second module is entitled to "Solution Structures of Spatial Data Science Problems", which is composed of four lectures and will give learners an overview of academic subjects, software tools, and their combinations for the solution structures of spatial data science problems. The first lecture, "Four Disciplines for Spatial Data Science and Applications" will introduce four academic disciplines related to spatial data science, which are Geographic Information System (GIS), Database Management System (DBMS), Data Analytics, and Big Data Systems. The second lecture "Open Source Software's" will introduce open source software's in the four related disciplines, QGIS for GIS, PostgreSQL and PostGIS for DBMS, R for Data Analytics, Hadoop and Hadoop-based solutions for Big Data System, which will be used throughout this course. The third lecture "Spatial Data Science Problems" will present six solution structures, which are different combinations of GIS, DBMS, Data Analytics, and Big Data Systems. The solution structures are related to the characteristics of given problems, which are the data size, the number of users, level of analysis, and main focus of problems. The fourth lecture "Spatial Data vs. Spatial Big Data" will make learner have a solid understanding of spatial data and spatial big data in terms of similarity and differences. Additionally, the value of spatial big data will be discussed.