Course Summary
This course is designed for learners who want to gain skills in spatial data science. It covers the fundamental concepts of spatial data science, spatial data structures and algorithms, and spatial data visualization. Students will learn how to analyze and visualize spatial data using R programming language.Key Learning Points
- Learn fundamental concepts of spatial data science
- Gain hands-on experience in R programming language
- Understand spatial data structures and algorithms
Job Positions & Salaries of people who have taken this course might have
- GIS Analyst
- USA: $49,000
- India: ₹423,000
- Spain: €25,000
- Spatial Data Scientist
- USA: $80,000
- India: ₹1,500,000
- Spain: €45,000
- Data Analyst
- USA: $62,000
- India: ₹575,000
- Spain: €35,000
Related Topics for further study
Learning Outcomes
- Understand the principles of spatial data science
- Gain proficiency in R programming language
- Learn to analyze and visualize spatial data
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with programming concepts
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Geographic Information Systems (GIS) Specialization
- Geospatial Intelligence & the Geospatial Revolution
Related Education Paths
Related Books
Description
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.
Outline
- Understanding Spatial Data Science
- Introduction to the course
- 1.1 Introduction to Spatial Data Science
- 1.2 Why is Spatial Special? (I) - A Business Perspective
- 1.3 Why is Spatial Special? (II) - A Technical Perspective
- 1.4 Why is Spatial Special? (III) - A Data Perspective
- Understanding Spatial Data Science
- Solution Structures of Spatial Data Science Problems
- Four Disciplines for Spatial Data Science and Applications
- Open Source Software's
- Spatial Data Science Problems
- Spatial Data vs. Spatial Big Data
- QGIS vs. ArcGIS
- What is spatial Big Data?
- Solution Structures of Spatial Data Science Problems
- Geographic Information System (GIS)
- Five Layers of GIS
- Spatial Reference Framework
- Spatial Data Models
- Spatial Data Acquisition
- Spatial Data Analysis
- Geo-visualization and Information Delivery
- Sources of Spatial Data
- Making Sense of Maps
- Geographic Information System (GIS)
- Spatial DBMS and Big Data Systems
- Database Management System (DBMS)
- Spatial Database Management System (SDBMS)
- Big Data System – MapReduce
- Big Data System – Hadoop
- Hadoop Ecosystem
- Spatial Big Data Systems
- DBMS vs. MapReduce
- Spatial DBMS and Big Data Systems
- Spatial Data Analytics
- Spatial Data Analytics
- Proximity and Accessibility
- Spatial Autocorrelation
- Spatial Interpolation
- Spatial Categorization
- Hotspot Analysis
- Network Analysis
- Starbucks GIS
- Happy Maps
- Spatial Data Analytics
- Practical Applications of Spatial Data Science
- Desktop GIS - Finding Optimal Counties for Timber Investment
- Server GIS - An Integration of Municipal Spatial Databases
- Spatial Data Analytics I - Influential Variables of Regional Disease Prevalence Rate
- Spatial Data Analytics II - Military Infiltration Route Analysis
- Spatial Big Data Management and Analytics - Taxi Trajectory Analysis for Finding Pick-up Hotspots
- Infiltration route analysis using Thermal Observation Devices
- Practical Application of Spatial Data Science
Summary of User Reviews
The Spatial Data Science course on Coursera is highly recommended by users. It provides a comprehensive understanding of spatial data science without overwhelming students with technical jargon. Many users found the course to be engaging and well-structured.Key Aspect Users Liked About This Course
The course is engaging and well-structured.Pros from User Reviews
- Comprehensive understanding of spatial data science
- No overwhelming technical jargon
- Good pacing and flow of course material
- Assignments are challenging but rewarding
- Instructors are knowledgeable and responsive
Cons from User Reviews
- Some users found the course material to be too basic
- Course content could be more hands-on
- No real-world case studies or applications provided
- Peer-graded assignments can be inconsistent
- Course forum can be slow to respond to user queries
Joon Heo
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