About this Course
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64 ratings
21 reviews
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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Intermediate Level

Intermediate Level

Clock

Suggested: 6 hours/week

Approx. 10 hours to complete
Comment Dots

English

Subtitles: English

Skills you will gain

Spatial AnalysisQgisBig DataGeographic Information System (GIS)
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Clock

Suggested: 6 hours/week

Approx. 10 hours to complete
Comment Dots

English

Subtitles: English

Syllabus - What you will learn from this course

1

Section
Clock
1 hour to complete

Understanding Spatial Data Science

The first module of "Spatial Data Science and Applications" is entitled to "Understanding of Spatial Data Science." This module is composed of four lectures. The first lecture "Introduction to spatial data science" was designed to give learners a solid concept of spatial data science in comparison with science, data science, and spatial data science. For Learner's better understanding, examples of spatial data science problems are also presented. The second, third, and fourth lectures focuses on "what is spatial special? - unique aspects of spatial data science from three perspectives of business, technology, and data, respectively. In the second lecture, learners will learn five reasons why major IT companies are serious about spatial data, in other words, maps. The third lecture will allow learners to understand four issues of dealing with spatial data, including DBMS problems, topology, spatial indexing, and spatial big data problems. The fourth lecture will allow learners to understand another four issues of spatial data including spatial autocorrelation, map projection, uncertainty, and modifiable areal unit problem....
Reading
5 videos (Total 45 min), 1 quiz
Video5 videos
1.1 Introduction to Spatial Data Science11m
1.2 Why is Spatial Special? (I) - A Business Perspective12m
1.3 Why is Spatial Special? (II) - A Technical Perspective9m
1.4 Why is Spatial Special? (III) - A Data Perspective8m
Quiz1 practice exercise
Understanding Spatial Data Science10m

2

Section
Clock
1 hour to complete

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....
Reading
4 videos (Total 46 min), 2 readings, 1 quiz
Video4 videos
Open Source Software's7m
Spatial Data Science Problems15m
Spatial Data vs. Spatial Big Data9m
Reading2 readings
QGIS vs. ArcGIS10m
What is spatial Big Data?10m
Quiz1 practice exercise
Solution Structures of Spatial Data Science Problems10m

3

Section
Clock
2 hours to complete

Geographic Information System (GIS)

The third module is "Geographic Information System (GIS)", which is one of the four disciplines for spatial data science. GIS has five layers, which are spatial reference framework, spatial data model, spatial data acquisition systems, spatial data analysis, and geo-visualization. This module is composed of six lecture. The first lecture "Five Layers of GIS" is an introduction to the third module. The rest of the lectures will cover the five layers of GIS, one by one. The second lecture "Spatial Reference Framework" will make learners understand, first, a series of formulation steps of physical earth, geoid, ellipsoid, datum, and map projections, second, coordinate transformation between different map projections. The third lecture "Spatial Data Models" will teach learners how to represent spatial reality in two spatial data models - vector model and raster model. The fourth lecture "Spatial Data Acquisition Systems" will cover topics on how and where to acquire spatial data and how to produce your own spatial data. The fifth lecture "Spatial Data Analysis", will make learners to have brief taste of how to extract useful and valuable information from spatial data. More advanced algorithms for spatial analysis will be covered in the fifth module. In the sixth lecture "Geovisualization and Information Delivery", learners will understand powerful aspects as well as negative potentials of cartographic representations as a communication media of spatial phenomenon. ...
Reading
6 videos (Total 82 min), 2 readings, 1 quiz
Video6 videos
Spatial Reference Framework22m
Spatial Data Models9m
Spatial Data Acquisition15m
Spatial Data Analysis11m
Geo-visualization and Information Delivery14m
Reading2 readings
Sources of Spatial Data10m
Making Sense of Maps10m
Quiz1 practice exercise
Geographic Information System (GIS)20m

4

Section
Clock
2 hours to complete

Spatial DBMS and Big Data Systems

The fourth module is entitled to "Spatial DBMS and Big Data Systems", which covers two disciplines related to spatial data science, and will make learners understand how to use DBMS and Big Data Systems to manage spatial data and spatial big data. This module is composed of six lectures. The first two lectures will cover DBMS and Spatial DBMS, and the rest of the lectures will cover Big Data Systems. The first lecture "Database Management System (DBMS)" will introduce powerful functionalities of DBMS and related features, and limitations of conventional Relational DBMS for spatial data. The second lecture "Spatial DBMS" focuses on the difference of spatial DBMS from conventional DBMS, and new features to manage spatial data. The third lecture will give learners a brief overview of Big Data Systems and the current paradigm - MapReduce. The fourth lecture will cover Hadoop MapReduce, Hadoop Distributed File System (HDFS), Hadoop YARN, as an implementation of MapReduce paradigm, and also will present the first example of spatial big data processing using Hadoop MapReduce. The fifth lecture will introduce Hadoop ecosystem and show how to utilize Hadoop tools such as Hive, Pig, Sqoop, and HBase for spatial big data processing. The last lecture "Spatial Big Data System" will introduce two Hadoop tools for spatial big data - Spatial Hadoop and GIS Tools for Hadoop, and review their pros and cons for spatial big data management and processing. ...
Reading
6 videos (Total 79 min), 1 reading, 1 quiz
Video6 videos
Spatial Database Management System (SDBMS)14m
Big Data System – MapReduce13m
Big Data System – Hadoop11m
Hadoop Ecosystem11m
Spatial Big Data Systems12m
Reading1 reading
DBMS vs. MapReduce10m
Quiz1 practice exercise
Spatial DBMS and Big Data Systems20m

Instructor

Joon Heo

Professor
School of Civil and Environmental Engineering

About Yonsei University

Yonsei University was established in 1885 and is the oldest private university in Korea. Yonsei’s main campus is situated minutes away from the economic, political, and cultural centers of Seoul’s metropolitan downtown. Yonsei has 3,500 eminent faculty members who are conducting cutting-edge research across all academic disciplines. There are 18 graduate schools, 22 colleges and 133 subsidiary institutions hosting a selective pool of students from around the world. Yonsei is proud of its history and reputation as a leading institution of higher education and research in Asia....

Frequently Asked Questions

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