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!