Learn Spatial Analysis in GIS with Python & R —High-quality Courses

Last Updated 4 days ago

Spatial Analysis is a booming niche…

It is simply looking at where things happen to understand why they happen there.

Geospatial Data Science is the discipline that specifically focuses on the spatial component of data science.

Currently, there are thousands of unique job level postings in Geospatial Data Science, and the top 5 job titles are;

– GIS Analysts
– Data Scientists
– Geospatial Analysts
– GIS Specialists
– Resource Engineers

Spatial Analysis is considered as a core infrastructure of the modern tech industry and is heavily substantiated by the business transactions of world-leading companies such as Uber, Deliveroo, Apple, Google, Intel, and evidently by the motor companies such as Tesla, BMW, and Mercedes.

So, these companies are bound to hire more and more Spatial Data Analysts and Geo-Spatial Scientists. 

Based on these business trends, we’ve compiled the spatial analysis courses designed by world-class educators to help beginners gain solid foundations of spatial data analysis.

And If you have a basic knowledge of programming for data science or data analysis, then these high-quality courses will edify you with the employable expertise differentiated from nominal data analysts.

Now, let’s get started.


17 Best Spatial Analysis Courses in GIS [ Including Python, R & Earth Engine ]

These spatial analysis courses will help you to see the bigger picture to better understand the value of spatial big data by using powerful open-source tools, libraries, and packages to deal with spatial data science problems.


Professional Certificate in Geographic Information Systems (GIS) Essentials

This high-quality course is suitable for learners who are interested in building proficiency in Spatial Analysis and problem solving using geoprocessing tools.

Through the guided series of lectures and hands-on exercises, you’ll learn to perform spatial analysis to communicate results through maps, graphs, and ArcGIS story maps.

You’ll learn thoroughly about the image processing and analysis in ArcGIS Pro to assess vegetation health, wildfire burn severity, and flood impacts.

Moreover, You’ll learn about image classification to map land use and land cover and upgrade your skills in geoprocessing tools for landscape analysis and watershed delineation.

Geographic Information Systems (GIS) Essentials edX
source: edX

Is it right for you?

This certification course is suitable for beginners without any prior experience in GIS or programming.

Career prospects for the Students from this professional certificate course include GIS Analysts, Geospatial Analysts, GIS Specialists

Upon successful completion, you’ll be equipped with the functional knowledge of performing Spatial Analysis in GIS and skills to proficiently use ESRI Tools, ArcGIS Pro, and ArcScene.

GO TO → Geographic Information Systems (GIS) Essentials

Fundamentals of GIS

This Intermediate-level course aims to help learners begin their journey into Spatial Analysis and Cartography with Geographic Information Systems by using Geo Processing Tools.

This course will help you to understand the core geospatial concepts and learn everything about analyzing data to make your maps.

Through the series of lectures and exercises, you’ll learn how to run geoprocessing tools for projections and furthermore increase in understanding to use more advanced mapping techniques to output map books.

Moreover, you’ll also learn how to visualize your data.

source: Coursera

Is it right for you?

This course is suitable for learners with adequate business skills and knowledge of analyzing data.

Upon successful completion, you’ll have a deeper understanding of Spatial Analysis and become highly prepared to learn advanced topics in the ‘Geographic Information Systems (GIS) Specialization’ offered by UC Davis.

GO TO → Fundamentals of GIS

Introduction to GIS Mapping

This beginner-friendly course provides an in-depth overview of the GIS system especially the projection systems in an easy-to-understand language.

Through the guided lectures, you’ll gain the basic knowledge of Spatial Analysis you need to start working with GIS to grasp the complex concepts presented in the other courses.

Introduction to GIS Mapping
source: Coursera

Is it right for you?

This interactive course mainly focuses on the theory to help beginners learn more about mapping and Geographical Information Systems.

By the end, you’ll have theoretical knowledge of how Geographic Information System (GIS) works and a basic understanding of Cartography, ESRI, Mapping, and Spatial Analysis using ArcGIS Desktop.

GO TO → Introduction to GIS Mapping

Start with Google Earth Engine & Spatial Analysis #Beginners

This beginner-level course aims to introduce learners to the basics of JavaScript for remote sensing and big geodata and spatial analysis.

You’ll get solid foundations to work with big data on the cloud, Earth Engine exploration, and understand how to work with satellite images and remote sensing.

In this course, you’ll become highly prepared to use open-source tools for working with Geospatial analysis.

Start with Google Earth Engine & Spatial Analysis #Beginners
source: Udemy

Is it right for you?

This course is suitable for Geographers, Programmers, geologists, biologists, social scientists, and anyone interested in GIS.

Upon successful completion, you’ll have a good knowledge and skills to perform Geospatial analysis with big data on the cloud and also in Google Earth Engine.

GO TO → Start with Google Earth Engine & Spatial Analysis #Beginners

Geographic Information Systems (GIS) Specialization

This beginner-level specialization is highly recommended for absolute beginners to learn everything about GIS from the field ion and includes 1-year student license for ArcGIS to gain advanced familiarity with GIS application in a professional setting.

First, You’ll begin with the fundamentals to learn the key concepts of Spatial Analysis and Cartography and get hands-on experience in using ArcGIS.

Next, you’ll spend a good amount of time diving deeper into learning common data types (such as raster and vector data), structures, quality, and storage to fully grasp and understand GIS Data Formats, their design, and quality.

Then, you’ll spend your time doing the quality work by applying the knowledge you acquired on Geospatial and Environmental Analysis with much focus on Analysis Tools and also work on a project which will increase your knowledge from project conception, through data retrieval, initial data management, and processing, to analysis products.

Furthermore, through the series of guided lectures and technical tasks, you’ll learn to gain advanced familiarity with spatial analysis and applications within GIS for Imagery, Automation, and Applications.

Finally, your efforts will see the light of the day in the capstone project where you’ll create a professional-quality GIS portfolio piece using a combination of data identification and collection, analytical map development with spatial analysis techniques.

Geographic Information Systems (GIS) Specialization 1
source: Coursera

Is it right for you?

This specialization is excellent for beginners to learn from the industry professionals to work with raster data and develop a processing workflow in ModelBuilder.

The knowledge you’ll gain in this specialization will help you to analyze spatial data, use cartography techniques to communicate results in maps, and collaborate with peers in GIS-dependent fields.

Upon successful completion, you’ll become highly equipped with the skills in developing and analyzing data for the Geospatial Analysis project.

Moreover, you’ll gain analysis skills in Satellite, Data Visualization, Spatial Visualization, Imagery Analysis, and more.

GO TO → Geographic Information Systems (GIS) Specialization

Professional Certificate in Spatial Computational Thinking

This high-quality course is offered by the National University of Singapore and delivered via edX to help learners build Computational Thinking, logical thinking, and problem-solving skills for Geospatial Data Science.

First, you’ll start with procedural modelling and learn everything important in procedural programming for generating spatial information models.

The first course will equip you with the skillset to own procedures using functions, data structures, and control-flow statements for spatial models.

Next, you’ll learn about Generative Modelling and understand everything you need to about generating spatial information models that help you to capture relationships and constraints.

The second course will equip you with the advanced skillset to create your own scripts consisting of multiple procedures that work together to generate complex spatial information models and with hands-on exercises, you’ll also create procedures that annotate and query your models using attribute data.

Finally, you’ll spend time learning about Performative modelling by evaluating alternative spatial models to support evidence-based decision-making.

Moreover, you’ll understand the methods of calculating various spatial performance metrics related to the built environment and will use these performance metrics to carry out the comparative analysis of design options.

Finally, you’ll solidify your automation skills by creating scripts for the process of generating and analyzing alternative design options.

Professional Certificate in Spatial Computational Thinking
source: edX

Is it right for you?

This course is highly recommended for learners who have experience in programming and basic knowledge of data analysis.

You’ll be able to proficiently work on procedural algorithms to use fundamental data structures and control-flow constructs for generating spatial information models.

You’ll also build an advanced understanding of Integrating multiple procedures that assist in generating complex spatial information models.

Moreover, you’ll be equipped in evaluating alternative spatial information models to provide insights for performance-based decision-making.

Upon successful completion of all three courses including exercises, you’ll have acquired sound skills to thrive in Geospatial Data Science which your professional certificate can validate.

GO TO → Professional Certificate in Spatial Computational Thinking

Visualizing Geospatial Data in R

This Interactive course aims to equip learners with skills to read, explore, and manipulate spatial data to create informative maps in R programming.

First, you’ll start with the basics of mapping and gain familiarity with ggplot2 and ggmap packages to work through the spatial data and learn how to add spatial context to your plots.

Next, you’ll learn to simplify the process with data frames, understand spatial object classes with tmap, and finish the module by creating a world population map.

Afterwards, you’ll work with the raster data and colour using sp package for the visual display, important for maps.

Finally, you’ll conclude by creating a visualization from raw spatial data files to adding credit to a map.

Visualizing Spatial Data in R
source: DataCamp

Is it right for you?

If you have knowledge of R programming and little experience in data visualization, then this course will help you to become highly equipped with solid skills to read spatial data into R, make projections and work with the coordinate reference systems.

By the end, you’ll have the skills to take more advanced Spatial Analysis Courses including Geospatial Data Science.

GO TO → Visualizing Geospatial Data in R

GIS, Mapping, and Spatial Analysis Specialization

This specialization is suitable for beginners to learn the concepts of Spatial Analysis and use the important tools and techniques to simplify mapping for answering geographic questions.

You’ll start with the Introduction to GIS Mapping and learn everything you need to about mapping and GIS and gain a better understanding of how it all works and why.

In the second course ‘GIS Data Acquisition and Map Design‘ you’ll learn to create your own projects, starting from the data discovery to designing your own quantitative maps.

And, Through the series of lectures, you’ll get a deeper understanding of building blocks of GIS data to designing cartographic principles.

In the third course, ‘Spatial Analysis and Satellite Imagery in a GIS‘ you’ll dive into analyzing map data by applying different methods of analysis to provide insights to complex Geographic questions. 

Finally, you’ll conclude this Specialization in the fourth course ‘GIS, Mapping, and Spatial Analysis Capstone’,  where you will apply everything you have learned in the previous courses by designing and then completing your own GIS project.

GIS Mapping and Spatial Analysis Specialization
source: Coursera

Is it right for you?

This specialization is suitable for absolute beginners to become highly skilled with the necessary skills in Spatial Analysis from the field ion.

You’ll learn about analyzing raster data and gain hands-on experience in powerful analysis methods of using vector data to find the spatial relationships within and between data sets.

Moreover, you’ll learn to use ModelBuilder for creating flowcharts to run as models that assist in finding, understanding the remotely sensed data such as satellite imagery.

Upon successful completion, you’ll be highly equipped with the employable skills to make your own products and perform spatial analysis.

GO TO → GIS, Mapping, and Spatial Analysis Specialization

QGIS and Google Earth Engine Python API for Spatial Analysis

This high-quality course helps learners to get hands-on experience in using Google Earth Engine with the Python API platform.

You’ll learn to access satellite data in Earth Engine and analyze that data including, MODIS, Sentinel, and Landsat to extract information and export geospatial data.

You’ll also learn about Cloud Masking Algorithm, Calculating NDVI: Normalized Difference Vegetation Index For Agriculture, Processing Image Collection, Clustering Analysis, Exporting Images and Videos through the guided series of lectures.

QGIS and Google Earth Engine Python API for Spatial Analysis
source: Udemy

Is it right for you?

This course is suitable for Intermediate learners who have experience in Python and basic knowledge of Machine Learning & Algorithms.

Upon successful completion, you’ll have skills in accessing and visualizing satellite data using Python and proficiency in using QGIS and Earth Engine plugin.

GO TO → QGIS and Google Earth Engine Python API for Spatial Analysis

Spatial Data Science and Applications

This high-rated course is offered by YONSEI University for Intermediates to gain advanced familiarity in Spatial Data Analysis for GIS, DBMS, Data Analytics, and Big Data Systems, including opensource software.

You’ll start with identifying problems to understand Spatial Data Science by learning spatial autocorrelation, map projection, uncertainty, and modifiable areal unit problem.

Afterwards, you’ll dive into the disciplines for Spatial Data Science and Applications related to GIS including but not limited to QGIS, PostgreSQL, PostGIS, R, and Hadoop.

Then, you’ll understand the layers of GIS along with all the key concepts and the techniques in Spatial Reference Framework, Spatial Data Models, Spatial Data Acquisition Systems, Spatial Data Analysis, and Geovisualization and Information Delivery.

Furthermore, you’ll cover the important concepts in Spatial Data Analytics through the series of guided lectures and exercises to understand the increasing importance of Spatial Big Data Analysis.

Finally, you’ll use every important opensource framework to apply your knowledge into practice to gain sound skills in data science applications used today by the world-leading tech companies.

Spatial Data Science and Applications
source: Coursera

Is it right for you?

The career outcomes for taking this course are that you’ll build a very strong foundation in Spatial Data Science to learn more advanced concepts.

Upon successful completion, you’ll have gained life-long skills in Spatial Analysis, Big Data, Qgis, Geographic Information Systems (GIS) in analyzing real-world problems and presenting step-by-step procedures in the environment of opensource software for solving them.

GO TO → Spatial Data Science and Applications

Spatial Analysis and Geospatial Data Science With Python

This Intermediate-level course will help you to learn the key concepts involved in the processing and visualizing geospatial data and use Python for Spatial Analysis.

You’ll be introduced to most libraries and packages to conduct spatial analysis in Python and learn to perform Geospatial Data Science operations.

And, You’ll also learn to use Jupyter Notebook for Geographic data analysis and build a strong foundational knowledge in Pandas.

Moreover, you’ll learn to build compelling and interactive Geospatial Data Visualizations using libraries like Geopandas, Folium, IpyLeaflet, and Plotly Express.

Finally, you’ll learn to work with advanced features including Geocoding, reverse geocoding, accessing OpenStreetMap data in Python, and processing large Geospatial datasets.

Spatial Analysis and Geospatial Data Science With Python
source: Udemy

Is it right for you?

This course is suitable for learners who have a good knowledge of Python and basic comfortability with using Python libraries to write data analysis functions.

By the end, you’ll have bootstrapped your knowledge and skills in Python for Geospatial Data Science and become highly prepared to work independently on Geospatial Projects including but not limited to the spatial analysis and advanced data visualization.

GO TO → Spatial Analysis and Geospatial Data Science With Python

Spatial Analysis with sf and raster in R

In this Intermediate-level course, you’ll learn about the sf package for working with vector data in R for more detailed and deep Spatial Analysis in R Programming.

First, you’ll learn comprehension methods in spatial data, manipulate vectors using the dplyr package, and through the guided exercised learn to work with coordinate reference systems.

Next, You’ll spend time gaining skills by preparing layers to conduct spatial analysis and performing geoprocessing of vectors.

And you’ll also learn how to aggregate, reclassify, crop, mask, and extract with rasters.

Finally,  you’ll learn how to make maps in R with the GGPLOT2 and tmap packages and get hands-on experience in Spatial Analysis by performing a fun mini-analysis to put your knowledge to work.

Spatial Analysis with sf and raster in R
source: DataCamp

Is it right for you?

If you have experience in R programming and comfortability in writing functions and using libraries, then this course will equip you with employable Spatial Analysis Skills.

By the end, you’ll have functional knowledge of working with vector data and R and experience in buffering, spatial joins, computing intersections, simplifying and measuring distance.

GO TO → Spatial Analysis with sf and raster in R

Spatial Analysis and Geospatial Data Science With Python

This highest-rated course introduces learners to the essential Geopython Libraries to perform Spatial Data analysis in Python.

Through the guided series of lectures and project exercises, you’ll learn to use Geopy and Plotly, often referred to as the workhorse of Geospatial data science in Python.

Spatial Analysis and Geospatial Data Science With Python
source: Udemy

Is it right for you?

This course is suitable for learners who have good experience in Python and familiarity of working with the libraries.

By the end, you’ll be skilled to visualize Geospatial data in Python (static and interactive maps) on your own using the spatial data.

GO TO → Spatial Analysis and Geospatial Data Science With Python

Analyzing US Census Data in R

This course helps learners to work with Census tabular and spatial data to visualize and explore demographic data using tidyverse package in the R environment.

First, you’ll learn to acquire data using tidycensus functions, search for data, and make a basic plot.

Next, you’ll understand how to handle margins of error in the ACS and work with geographic data in using the tigris package in R.

Finally, you’ll learn to make a customized static and interactive map of US Census data by using feature geometry with the tidycensus package, ggplot2, and mapview.

source: DataCamp

Is it right for you?

This course is suitable for learners with a background in R and familiarity with Tidyverse, sf package, and raster in R.

Upon successful completion, you’ll have the advanced familiarity of working with Spatial Data for Spatial Analysis.

GO TO → Analyzing US Census Data in R

Core Spatial Data Analysis: Introductory GIS with R and QGIS

This high-quality course aims to provide clarity of basic spatial data concepts and data types to beginners to gain skills by working on a real-life conservation-related spatial data analysis project.

You’ll learn to process raster and vector data in both R and QGIS to analyze spatial data and start with analyzing spatial data for your own projects using freeware tools.

Most importantly, you’ll focus on learning the most important and widely encountered spatial data analysis tasks.

Core Spatial Data Analysis Introductory GIS with R and QGIS
source: Udemy

Is it right for you?

This beginner-friendly course is suitable for learners who change working directories, install packages, load libraries and read in CSVs in R.

By the end, you’ll be able to answer geographical questions through spatial analysis by working on your own project and build a solid foundation to carry out practical spatial data analysis tasks in popular and FREE software frameworks.

GO TO → Core Spatial Data Analysis: Introductory GIS with R and QGIS

[Intermediate] Spatial Data Analysis with R, QGIS & More

This course uses Open Source GIS tools and covers key concepts to equip learners with the skills in map-making by leveraging the powerful libraries and packages available including Google earth.

You’ll also learn to perform advanced spatial data analysis using freeware tools like GRASS,  GIS and develop mapping skills in both R and QGIS

The series of guided lectures will help you to gain a solid foundation to perform advanced GIS tasks and you’ll also gain theoretical knowledge in preliminary geo-statistics and mapping/visualizing spatial data.

Spatial Data Analysis with R QGIS Moree
source: Udemy

Is it right for you?

This course is suitable for learners who have experience in R and have a basic knowledge of GIS systems.

By the end, you’ll have a strong proficiency in Spatial Analysis using R with functional knowledge of using freeware tools and other packages practically used for implementing more complex GIS tasks.

GO TO → [Intermediate] Spatial Data Analysis with R, QGIS & More

Spatial Data Analysis in Google Earth Engine Python & Colab

This Intermediate-level course equips learners in Spatial Analysis by using various applications of machine learning algorithms in the Earth Engine Python API and Colab.

You’ll learn to use Earth Engine Python API and Google Colab to access and visualize satellite data and access images and image collections from the Earth Engine API.

Moreover, you learn to analyze geospatial data including raster and vector data formats, and also learn to derive insights from real big Earth observation data which you will fetch from various sources including Landsat, MODIS, Sentinel-2, SRTM, and other remote sensing products.

Spatial Data Analysis-in Google Earth Engine Python Colab
source: Udemy

Is it right for you?

This course is suitable for learners with a background in Python and familiarity with accessing and reading data through the API and packages.

By the end, you’ll be equipped to jump-start your career in Geospatial Data Science with functional knowledge of the Machine Learning Applications involved.

GO TO → Spatial Data Analysis in Google Earth Engine Python & Colab

Closing Notes

If you find this compilation helpful, you might also benefit from the resources we’ve already compiled… ↓

Statistics for Data Science ( Relevant for Geospatial Analysis )

Math for Data Science ( Helpful for Geospatial Data Science )

Machine Learning Courses ( For Advanced Geospatial Learning )

Thanks for making it to the end ;)

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