Welcome to EarthCube Interactive Workshops!

These short courses will teach you how to conduct reproducible research using Data Sciences tools. You will be working with Python, Jupyter, Docker containerization, and Git for version control.

After these modules, you will be able to process your own research in a format suitable for analysis, writing your own analysis functions, and deriving data-driven insights via Jupyter Notebooks and RMarkdown files.

This page runs on a python3 kernel. To visit the R kernel version, click here.

Module 1: Intro to Python and Jupyter Notebooks

In this module, you will learn how to write basic Python code and how to use your Jupyter Notebooks.

Module 2: Setting up your working environment with conda

This module will teach you how to organize your environments using conda

Module 3: Intro to GitHub

This module will teach you about Version Control and how to use Git and Github.

Module 4: Introduction to Binder

This module will teach you how to use the Binder properly guaranteeing reproducibility of your notebooks and repositories.

Module 5: Working with RMarkdown

In this module you will create, edit and run reproducible R code documents using RMarkdown.

Module 6: Introduction to Docker

This module will teach you about Docker and containerization. You will be able to launch third party containers and write your own Dockerfile.

Module 7: Development best-practices including FAIR data principles

This module will teach you different aspects in regards to Data Workflows, handling filenames and best practices such as testing, continuous integration and licensing.

Module 8: Argovis

This module will help you work with the Argovis Database and showcase a detailed example. This Module has been produced by the Argovis team.

References and Resources

Here are scripts to re-use and links to additional references and topics to learn.

About this course

This is a free, open source course on how to use different Data Science Tools such as Jupyter Notebooks, Rmd, Docker, and Git. It's made possible by a long and fruitful collaboration and possible thanks to NSF and EarthCube. Contributions and comments on how to improve the course are welcome! To file an issue go to:

About me

For nearly a decade, the EarthCube community has been transforming the conduct of geosciences research by developing and maintaining a well-connected and facile environment that improves access, sharing, visualization, and analysis of data and related resources. While sharable tools, methods, and cyberinfrastructure have been critical achievements for EarthCube, we find that our dedicated community is what makes our program successful.