Spatial Data Science and Applications from Coursera

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Syllabus

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.

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.

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.

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.

Spatial Data Analytics

The fifth module is entitled to "Spatial Data Analytics", which is one of the four disciplines related to spatial data science. Spatial Data Analytics could cover a wide spectrum of spatial analysis methods, however, in this module, only some portion of spatial data analysis methods will be covered. The first lecture is an introduction, in which an overview of Spatial Data Analytics and a list of six topics are given and discussed. The second lecture "Proximity and Accessibility" will make learners realize how spatial data science can be used for business applications, while trade area analysis, supply to demand ratio, Floating Catchment Analysis (FCA), and Gravity-based index of accessibility are introduced and applied to real world problems. The third lecture "Spatial Autocorrelation" will give an instruction on how to measure spatial autocorrelation and to apply hypothesis test with Moran's I. The fourth lecture "Spatial Interpolation" will introduce trend surface analysis, inverse distance weighting, and Kriging. Particularly, in-depth explanations regarding Kriging, a de facto standard of spatial interpolation will be presented. The fifth lecture "Spatial Categorization" will make learners understand classification algorithms such as Minimum Distance to Mean (MDM) and Decision Tree (DT), clustering algorithms such as K-Means and DBSCAN with real-world examples. The sixth lecture "Hotspot Analysis" will introduce hotspot analysis and Getis-Ord GI* as the most popular method. The seventh lecture "Network Analysis" will make learners explore the algorithms of geocoding, map matching, and shortest path finding, of which importance is increasing in spatial big data analysis.

Practical Applications of Spatial Data Science

The sixth module is entitled to "Practical Applications of Spatial Data Science", in which five real-world problems are introduced and corresponding solutions are presented with step-by-step procedures in the solution structures and related open source software's, discussed in Module 2. The first lecture presents an example of Desktop GIS, in which only QGIS is used, to find the top 5 counties for timberland investment in the southeastern states of the U.S, in which simple differencing of demand and supply is applied to figure out counties of large deficit of timber supply in comparison with timber demand. In the second lecture, an example of sever GIS, in which QGIS and PostgreSQL/PostGIS are used, will be presented as a solution for a given problem of NYC spatial data center, which required multiple user access and different levels of privileges. The third lecture presents an example of spatial data analytics, in which QGIS and R are used, to find out any regional factors which contribute to higher or lower disease prevalence in administrative districts, for which spatial autocorrelation analysis is conducted and decision tree analysis is applied. The fourth lecture is another example of spatial data analytics, to find optimal infiltration routing with network analysis, in which cost surface is produced and Dijkstra's algorithm is used. The fifth lecture is an example of spatial big data management and analytics, in which QGIS, PostGIS, R, and Hadoop MapReduce are all used, to provide a solution of "Passenger Finder", which can guide to the places where more passengers are waiting for taxi cabs. For the solution, spatial big data, taxi trajectory, are collected, and noise removal and map matching are conducted in Hadoop environment. Then, a series of spatial data processing and analysis such as spatial join in PostGIS, hotspot analysis in R are conducted in order to provide the solution. All in all, learners will realize the value of spatial big data and power of the solution structure with combination of four disciplines.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a strong conceptual foundation for spatial data science and its applications

Taught by Joon Heo, an expert in spatial data science and geographic information science

Applicable to various fields and industries, including business, technology, and data

Uses open-source software (QGIS, PostgreSQL, PostGIS, R, Hadoop) for hands-on practice

Covers real-world problems and practical applications of spatial data science

Assumes basic knowledge of data science and data analysis

Reviews summary

Well-regarded spatial data science overview

Learners say this is a well-received introductory course in spatial data science that provides a solid overview of the field. Students remarked on engaging assignments, informative examples, and a knowledgeable instructor. The course was praised for its clear explanations and practical approach.

Learners appreciate the hands-on assignments that apply course material.

"The assignments were very helpful in reinforcing the concepts I learned in the lectures."

"I really enjoyed the hands-on assignments."

"The assignments were challenging but fair."

Students enjoy the clear and engaging lectures.

"The lectures were very clear and informative."

"I really enjoyed the instructor's teaching style."

"I learned a lot from this course."

Students commend the instructor's knowledge and enthusiasm.

"The instructor was very knowledgeable and enthusiastic."

"The instructor was always willing to answer my questions."

"I really enjoyed learning from the instructor."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Spatial Data Science and Applications with these activities:

Review fundamental concepts in data science and analysis

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Ensure a solid foundation by refreshing your knowledge of data science and analysis concepts.

Browse courses on Data Science

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Review textbooks or online resources on data science and analysis fundamentals.
Work through practice problems or exercises to reinforce your understanding.

Follow online tutorials on spatial data science tools and technologies

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Expand your knowledge of spatial data science tools and technologies.

Browse courses on GIS

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Identify reputable online platforms or websites that offer tutorials on spatial data science tools and technologies.
Select tutorials that align with your interests and learning goals.
Follow the tutorials carefully, taking notes and practicing the concepts you learn.

Attend spatial data science conferences or meetups

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Connect with professionals in the field and learn about emerging trends.

Browse courses on GIS

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Identify upcoming spatial data science conferences or meetups in your area or online.
Register and attend the events, actively participating in discussions and networking with attendees.

Four other activities

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Complete spatial data science exercises

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Reinforce your understanding of spatial data science concepts by completing a series of exercises.

Browse courses on GIS

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Find online resources or textbooks that provide spatial data science exercises.
Work through the exercises carefully, paying attention to the instructions and explanations.
Check your answers against provided solutions or ask a mentor or instructor for feedback.

Participate in a spatial data science workshop or bootcamp

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Acquire new skills or enhance existing ones through hands-on training.

Browse courses on GIS

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Research and identify reputable spatial data science workshops or bootcamps that align with your learning goals.
Enroll in the workshop or bootcamp and actively participate in the learning activities.

Create a spatial data science portfolio

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Solidify your understanding of the course materials by creating a portfolio of spatial data science projects.

Browse courses on GIS

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Identify real-world problems that can be solved using spatial data science.
Collect and prepare the necessary data for each project.
Analyze the data using appropriate spatial data science techniques.
Develop and implement solutions to the problems.
Present your findings and solutions in a clear and concise manner.

Develop a spatial data science blog or website

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Deepen your understanding of spatial data science by teaching it to others.

Browse courses on GIS

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Choose a topic or niche within spatial data science that you are passionate about.
Research and gather information on the topic, ensuring accuracy and credibility.
Plan and structure your blog or website content, using clear and concise language.
Create high-quality written, visual, or multimedia content that engages your audience.
Promote your blog or website through social media, networking, and other channels.

Career center

Learners who complete Spatial Data Science and Applications will develop knowledge and skills that may be useful to these careers:

Geospatial Analyst

A geospatial analyst uses geographic information systems (GIS) to analyze and interpret data, and to create maps and other visualizations. This course provides a strong foundation in the principles of GIS and spatial data analysis, including data acquisition, manipulation, and modeling. It also covers advanced topics such as spatial autocorrelation, interpolation, and network analysis. These skills are essential for geospatial analysts who want to work in a variety of fields, including environmental science, urban planning, and transportation.

See salaries and explore the career path for Geospatial Analyst

GIS Specialist

A GIS specialist uses GIS software to create and manage geographic data. This course provides a comprehensive overview of the GIS workflow, from data acquisition and preparation to analysis and visualization. Students will learn how to use GIS software to create maps, perform spatial analysis, and solve real-world problems. This course is ideal for GIS specialists who want to work in a variety of fields, including environmental science, urban planning, and transportation.

See salaries and explore the career path for GIS Specialist

Data Analyst

A data analyst uses data to identify trends and patterns, and to make recommendations for businesses. This course provides a strong foundation in the principles of data analysis, including data collection, cleaning, and analysis. It also covers advanced topics such as machine learning and predictive analytics. These skills are essential for data analysts who want to work in a variety of fields, including finance, marketing, and healthcare.

See salaries and explore the career path for Data Analyst

Data Scientist

A data scientist uses data to solve complex problems and make predictions. This course provides a comprehensive overview of the data science workflow, from data acquisition and preparation to modeling and evaluation. Students will learn how to use data science tools and techniques to extract insights from data and make informed decisions. This course is ideal for data scientists who want to work in a variety of fields, including finance, marketing, and healthcare.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A machine learning engineer designs and builds machine learning models. This course provides a strong foundation in the principles of machine learning, including data preprocessing, model selection, and evaluation. It also covers advanced topics such as deep learning and reinforcement learning. These skills are essential for machine learning engineers who want to work in a variety of fields, including finance, marketing, and healthcare.

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Software Engineer

A software engineer designs, develops, and maintains software applications. This course provides a strong foundation in the principles of software engineering, including software design, development, and testing. It also covers advanced topics such as cloud computing and mobile development. These skills are essential for software engineers who want to work in a variety of fields, including finance, marketing, and healthcare.

See salaries and explore the career path for Software Engineer

Web Developer

A web developer designs and develops websites and web applications. This course provides a strong foundation in the principles of web development, including HTML, CSS, and JavaScript. It also covers advanced topics such as web design and development frameworks. These skills are essential for web developers who want to work in a variety of fields, including finance, marketing, and healthcare.

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Database Administrator

A database administrator designs, implements, and maintains databases. This course provides a strong foundation in the principles of database management, including data modeling, data storage, and data indexing. It also covers advanced topics such as database security and performance tuning. These skills are essential for database administrators who want to work in a variety of fields, including finance, marketing, and healthcare.

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Business Analyst

A business analyst analyzes business processes and systems, and makes recommendations for improvements. This course provides a strong foundation in the principles of business analysis, including data collection, analysis, and modeling. It also covers advanced topics such as process mapping and optimization. These skills are essential for business analysts who want to work in a variety of fields, including finance, marketing, and healthcare.

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Project Manager

A project manager plans, executes, and closes projects. This course provides a strong foundation in the principles of project management, including project planning, scheduling, and budgeting. It also covers advanced topics such as risk management and stakeholder management. These skills are essential for project managers who want to work in a variety of fields, including finance, marketing, and healthcare.

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Technical Writer

A technical writer creates documentation for software and other technical products. This course provides a strong foundation in the principles of technical writing, including documentation planning, writing, and editing. It also covers advanced topics such as user experience design and content management. These skills are essential for technical writers who want to work in a variety of fields, including finance, marketing, and healthcare.

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Information Architect

An information architect designs and organizes the structure and content of websites and other digital products. This course provides a strong foundation in the principles of information architecture, including user experience design, content strategy, and navigation design. It also covers advanced topics such as search engine optimization and accessibility. These skills are essential for information architects who want to work in a variety of fields, including finance, marketing, and healthcare.

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User Experience Designer

A user experience designer designs the user interface and user experience for websites and other digital products. This course provides a strong foundation in the principles of user experience design, including human-computer interaction, usability testing, and prototyping. It also covers advanced topics such as mobile design and accessibility. These skills are essential for user experience designers who want to work in a variety of fields, including finance, marketing, and healthcare.

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Interaction Designer

An interaction designer designs the way users interact with digital products. This course provides a strong foundation in the principles of interaction design, including user research, prototyping, and evaluation. It also covers advanced topics such as mobile design and accessibility. These skills are essential for interaction designers who want to work in a variety of fields, including finance, marketing, and healthcare.

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Graphic designer

A graphic designer creates visual content for websites, print, and other media. This course provides a strong foundation in the principles of graphic design, including typography, color theory, and layout. It also covers advanced topics such as web design and branding. These skills are essential for graphic designers who want to work in a variety of fields, including finance, marketing, and healthcare.

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