OERs in the Context of this Course#

Having had the experience of writing and publishing a book following the traditional, for-profit approach, I decided to build this new course on an OER model. To that end, I am writing much of the material myself, and releasing it under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. This license allows anyone to share or adapt the material as they wish. However, it is a copyleft license, meaning that any future use requires that I be attributed as an original author, that it not be used for commercial purposes, and that any modifications be distributed under the same license as the original. Beyond this, I have aimed to use OERs and otherwise freely-available resources wherever possible.

This course is, however, a hybrid of different tools using different licenses and restrictions. For example, we use Python, which is free software released under a GPL-compatible license. This book, and the data and code used in the course, are hosted on GitHub, which is a private corporation owned by Microsoft, but is free to use and, indeed, supports the distribution of much of the world’s free and open software. For running Python and doing our coursework, we will use the CoCalc platform. This is a paid service (much like we pay for materials in other lab classes), but it is the best available tool for running this course. As well, I personally like CoCalc’s business model, which is to use the subscription fees to support the creator’s work on the project, and also on supporting an important open source project, SageMath.

This course also uses a lot of lessons on Python and data science from DataCamp, which is another for-profit company. DataCamp kindly makes their entire platform free to use by university professors in the context of running courses. So, while it is not a truly open platform, it is free for our purposes — although I recognize that people who read this textbook, but are not enrolled in a university course that provides DataCamp access, may not be able to avail themselves of this resource for free. And, DataCamp aims to “democratize” data science so even outside this course context, you can do some of the lessons for free.

A final way this course uses OER is by encouraging you to find OER on your own, to support your learning in this course. You surely do this all the time anyway, finding free resources on the web to educate yourself about topics (academic or otherwise), help with homework, etc.. For programming and data science, there are vast resources freely available on the web, ranging from the official documentation for Python, to blog posts, to YouTube videos, to help forums. “Real” programmers and data scientists rely on these resources every day, and you will too!

In sum, this course is designed to encourage and embrace free software, open software, open science practices, and open educational resources. The core aim of this is to ensure that this course is as accessible as possible, particularly in reducing costs to you, as a student, and to the university (other than human resources costs). At the same time, I take a pragmatic approach to combining truly open resources created and distributed with no financial motivation, with other tools that are free for us to use, but are distributed under more restrictive licenses and/or by for-profit companies, and with subscription fees.