How to Learn Geospatial data science for free in 2020

Free online courses to learn the state of the art Geospatial data science.

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Geospatial data science is a booming niche. However, learning Geospatial data science can be a daunting task for both novice and intermediate users. The learning resources and path for this specialized field are less known and less shared compared to mainstream data science resources. I know how painful and disappointing it is to look for Geospatial data science resources in major MOOC providers like Courser, EDX, etc…

Geographic data science is the discipline that specifically focuses on the spatial component of the data science. It brings forth theories, concepts and applications that are specific to geographic data in the realm of data science.

In this article, I share the most up to date and free courses that can help you achieve your learning goals in Geospatial data science world. You will find a gem of valuable resources to kick start your career in Geospatial data science.

The resources included in this list are mainly in the Python ecosystem. We also include both beginner and advanced level resources on this list.

1. Geo Python (Helsinki University — latest release 2019)

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This course is a great place to kick-start your journey into Python programming for Geospatial data. You will learn the python programming fundamentals with a specific focus in Geospatial applications. Basic data types in Python, data processing and visualization are covered in this course. All materials including Lecture videos, Jupyter notebooks and GitHub exercises are open source and can be accessed freely.

What I like the most of this course is that it is not only user-friendly for beginners but also teaches you the state of the art technologies and tools used in the data science world including Jupyter notebooks. I highly recommend starting here if you are new to either Python or the Geospatial world.

Materials are available at Course Home page

2. Geographic Data Science (Liverpool University — latest release 2019)

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Geographic Data Science(ENVS363/563) is a well-structured course with a lot of practical applications in the Geospatial data science domain. The course has two main components: lectures and labs. The main topics covered in this course include both data science foundations and machine learning applications with Geospatial data.

Although there are no video lectures, the slides of the lectures are freely available. The labs with accompanying Jupyter notebooks are also open source and offer a lot of detailed work throughs on different aspects of Geospatial data science.

Course Home page.

3. Automating GIS Processes (Helsinki University — latest release 2019)

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This course is follow up to the first course in this list, Geo python and all its resources are freely available online. Automating GIS-processes has tutorials on how to perform some common GIS tasks in Python programming language. It also offers hosted Jupyter notebooks (Binder) that you can interact in the browser without the hassle of setting up your programming environment. This is an intermediate course that assumes knowledge in Python language.

Materials for the course: Course Home page.

4. Spatial Data Science (Chicago University — 2017)

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This is an advanced course with well-detailed explanations on the theoretical underpinnings on many spatial statistics concepts. Topics covered in this course include Exploratory Spatial Data Analysis( ESDA), Spatial regression, and unsupervised cluster for Geospatial data.

The labs of this course use Geoda software, but with the help of Pysal — Python Spatial Analysis Library— functionalities, implementing most of the lab exercises in Python is doable and a great hands-on project to enhance your understanding.

Lecture Videos: Youtube

5. Spatial Data Science and Applications (Coursera)

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The final course in this list highlights high-level applications of Geospatial data science with a variety of examples and applications in the world of spatial big data. Although this course does not have any programming tasks, it is a great introduction to the real-world applications of spatial data science, including tools used and step-by-step procedures in open source solutions.

Link to the course: Coursera.

Conclusion

The list highlights best and recent Geospatial data science courses. In another post, I will share with the best recent books on Geospatial data science. Let me know if you have any other additional courses in the field I did not include in this list.

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