Geospatial Data Science with R
Geospatial Data Science with R, available at $64.99, has an average rating of 4.85, with 47 lectures, 3 quizzes, based on 49 reviews, and has 395 subscribers.
You will learn about Hands-on learning with step-by-step code walkthroughs after each lecture Fully downloadable code notebooks complete with scripts, data processing workflows, and accompanying explanations All slides available as downloadable PDF No prior coding experience needed! Set up the computing environment for R programming following best practices Utilize RStudio, R Projects and R Markdown Notebooks for coding efficiency and reproducibility Use appropriate syntax, data structures, functions and software packages in R Understand and differentiate between various representations of spatial data, such as vector and raster formats Load, process and export spatial datasets, including those that exceed available memory (RAM) Select and use the appropriate coordinate reference systems Apply geometry and spatial operations to manipulate vector data Manipulate and summarize raster data to extract information from satellite imagery and other sources Create interactive and publication-ready maps and visualizations Develop workflows to automatically process and visualize geospatial data Apply techniques learnt to real-world problems in environmental monitoring and population demography This course is ideal for individuals who are Beginners who want to use geospatial data as a stepping stone into coding or Data scientists, researchers or developers who want to start working with spatial data and the open-source geospatial ecosystem or Geospatial or GIS professionals seeking to automate and enhance the reliability of their work It is particularly useful for Beginners who want to use geospatial data as a stepping stone into coding or Data scientists, researchers or developers who want to start working with spatial data and the open-source geospatial ecosystem or Geospatial or GIS professionals seeking to automate and enhance the reliability of their work.
Enroll now: Geospatial Data Science with R
Summary
Title: Geospatial Data Science with R
Price: $64.99
Average Rating: 4.85
Number of Lectures: 47
Number of Quizzes: 3
Number of Published Lectures: 47
Number of Published Quizzes: 3
Number of Curriculum Items: 50
Number of Published Curriculum Objects: 50
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Hands-on learning with step-by-step code walkthroughs after each lecture
- Fully downloadable code notebooks complete with scripts, data processing workflows, and accompanying explanations
- All slides available as downloadable PDF
- No prior coding experience needed!
- Set up the computing environment for R programming following best practices
- Utilize RStudio, R Projects and R Markdown Notebooks for coding efficiency and reproducibility
- Use appropriate syntax, data structures, functions and software packages in R
- Understand and differentiate between various representations of spatial data, such as vector and raster formats
- Load, process and export spatial datasets, including those that exceed available memory (RAM)
- Select and use the appropriate coordinate reference systems
- Apply geometry and spatial operations to manipulate vector data
- Manipulate and summarize raster data to extract information from satellite imagery and other sources
- Create interactive and publication-ready maps and visualizations
- Develop workflows to automatically process and visualize geospatial data
- Apply techniques learnt to real-world problems in environmental monitoring and population demography
Who Should Attend
- Beginners who want to use geospatial data as a stepping stone into coding
- Data scientists, researchers or developers who want to start working with spatial data and the open-source geospatial ecosystem
- Geospatial or GIS professionals seeking to automate and enhance the reliability of their work
Target Audiences
- Beginners who want to use geospatial data as a stepping stone into coding
- Data scientists, researchers or developers who want to start working with spatial data and the open-source geospatial ecosystem
- Geospatial or GIS professionals seeking to automate and enhance the reliability of their work
Embark on an exciting journey into the world of geospatial data science, and open up new possibilities for your research, business and projects. This course will equip you with the necessary skills to analyze, manipulate, and visualize spatial data using powerful tools and software libraries within the open-source R geospatial ecosystem.
What you’ll learn:
Throughout the course, I will guide you step-by-step to achieve the following learning objectives:
-
Set up R Environment:Follow best practices in setting up your computing environment using RStudio, R Projects and R Markdown Notebooks
-
R Fundamentals:Utilize appropriate syntax, data structures, functions and software packages for the given analysis
-
Understand Spatial Data:Recognise the differences between vector and raster formats, and how various types of spatial data can be represented and analyzed
-
Handle Spatial Datasets: Learn the techniques to load, process, and export spatial datasets, even when dealing with large files that exceed available memory (RAM)
-
Coordinate Reference Systems: Learn the importance of coordinate reference systems (CRS) and be able to select and apply the appropriate CRS for your analyses
-
Vector Data: Apply geometry and spatial operations to manipulate vector data
-
Raster Data: Manipulate and summarize raster data to extract information from satellite imagery and other sources
-
Data Processing Workflows: Develop scripts to automatically process and visualize geospatial data
-
Create Engaging Visualizations: Develop publication-ready maps and visualizations to effectively communicate your findings to a broader audience
-
Practical Applications: Apply your newfound skills to perform environmental monitoring and analyze population demography
This course comes with:
-
Comprehensive slides: Access all slides, which include example code and links to resources
-
Hands-on Learning:Step-by-step code walkthroughs after each lecture
-
Code Notebooks: Complete with scripts, data processing workflows, and accompanying explanations
-
Quizzes and Exercises: Strengthen and test your understanding of concepts that you’ve learnt
-
Lifetime Access: Enjoy unlimited access to all future updates
-
Udemy Certificate of Completion
-
Risk-free Learning: A 30 Day “No Questions Asked” Money Back Guarantee!
About your instructor:
Hello, I’m Xiao Ping (XP). In my professional journey, I have been deeply involved in developing metrics and predictive software for city planning and sustainability reporting. My research and teaching focus on applied machine learning and geospatial techniques. Throughout my career, I have taught bachelor- and master-level courses, coding workshops and music classes, and have had the privilege of receiving multiple teaching awards.
As an educator, I find that students are best motivated when they grasp underlying concepts and are inspired by what they see. That’s why, in our class, we will dive right into interesting and practical examples. We will take a hands-on approach, by actively applying our knowledge to real-world scenarios through a step-by-step process.
Are you ready?
What sets this course apart from typical data science offerings is our unique focus on spatial problems. Spatial problems offer a visually rich landscape for exploration and analysis, and in this course, we’ll immerse ourselves in engaging, hands-on examples. Whether you’re an absolute beginner or a seasoned professional, this course is designed for you to ground your understanding and gain practical skills that can be put into action immediately. Join me as we embark on this new journey of learning—I look forward to seeing you in class!
Sincerely,
Xiao Ping (XP)
Course Curriculum
Chapter 1: Introduction
Lecture 1: Welcome
Lecture 2: A quick heads-up
Lecture 3: Install R and RStudio
Lecture 4: Install R and RStudio: Resources
Lecture 5: Download course resources
Chapter 2: Introduction to R Programming
Lecture 1: About R
Lecture 2: Getting started
Lecture 3: R Notebooks: A heads-up on package installation
Lecture 4: R Notebooks
Lecture 5: R Projects
Lecture 6: General syntax
Lecture 7: Data structures
Lecture 8: Subsetting operations
Lecture 9: Functions
Lecture 10: The tidyverse: A heads-up on package installation
Lecture 11: The tidyverse
Lecture 12: The tidyverse: Example
Lecture 13: Week 1: Notes and exercises
Lecture 14: Reading: Additional resources
Chapter 3: R as a Geographical Information System
Lecture 1: Representing spatial data
Lecture 2: Set up R environment
Lecture 3: Working with vectors
Lecture 4: Vectors: Points
Lecture 5: Vectors: Lines
Lecture 6: Vectors: Polygons
Lecture 7: Vectors: Geometry operations
Lecture 8: Vectors: Spatial operations
Lecture 9: Working with rasters
Lecture 10: Convert between vectors and rasters
Lecture 11: Week 2: Notes and exercises
Lecture 12: Reading: Additional resources
Chapter 4: Practical Applications of Geospatial Analyses
Lecture 1: Section overview
Lecture 2: A note about multi-layered rasters
Lecture 3: Land cover classification: Overview
Lecture 4: Land cover classification: Process images
Lecture 5: Land cover classification: Classify images
Lecture 6: Land cover classification: Combining images
Lecture 7: Land cover classification: Practice exercise
Lecture 8: Land cover classification: Notes and exercises
Lecture 9: Dasymetric mapping: Overview
Lecture 10: Dasymetric mapping: Process population
Lecture 11: Dasymetric mapping: Process land use
Lecture 12: Dasymetric mapping: Rasterize and map
Lecture 13: Dasymetric mapping: Practice exercise
Lecture 14: Dasymetric mapping: Notes and exercises
Chapter 5: Conclusion
Lecture 1: Congratulations
Lecture 2: Bonus Lecture
Instructors
-
Dr. Xiao Ping (XP) Song
Instructor
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 3 votes
- 4 stars: 10 votes
- 5 stars: 36 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024