Geospatial Data Science with R

Description

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)

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

TAKE THIS COURSE