Portable learning environments for hands-on computational instruction: Using container- and cloud-based technology to teach data science

TL;DR

This paper explores how cloud and container technologies can create portable, effective hands-on learning environments for data science education outside traditional classrooms, enhancing experiential learning and reproducibility.

Contribution

It demonstrates recent advances in using cloud and container-based solutions for data science instruction, providing practical guidance and discussing benefits and challenges.

Findings

Remote server environments improve accessibility for students.

Containerization enhances reproducibility of computational experiments.

Cloud-based setups facilitate scalable and flexible learning experiences.

Abstract

There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the efficiency and ease with which students can learn. This manuscript details recent advances towards using commonly-available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benefits (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.