Teaching Data Science with Open Resources

This is the latest in our current series of short essays by participants in the Open Knowledge Fellowship coordinated by the Mina Rees Library. Fellows share insight into the process of converting a syllabus to openly-licensed and/or zero-cost resources, as well as their experiences teaching undergraduate courses at CUNY.

Filipa Calado is a PhD Candidate in the English Program at the CUNY Graduate Center. Her dissertation explores how digital methods and tools can be used with Queer Theory frameworks to study literature. She teaches courses on Coding Natural Language, Data Science, Digital Humanities, and Critical Thinking at CUNY, The New School, and NYU.



For this fellowship, I worked on developing an openly licensed course website for my “Foundations of Data Science” course at City College, CUNY. This course is designed to teach programming skills through data analysis methods with a focus on critical feminist approaches to studying data. Because it fulfills a quantitative reasoning curriculum requirement, the students in the course mostly come from humanities backgrounds, with little to no experience in programming. Throughout the semester, students learn to code through data collection and analytical methods, examining in particular how bias infiltrates computational processes. Then, they learn to use these same methods to deconstruct, critique, and challenge the ways that power and privilege structure our understanding and work with data.

My goal with the fellowship was to create a website that could host all of the programming lessons which we complete in class in an easily accessible way for students. I also wanted the website to be useful as an asynchronous learning tool. This is particularly important in a context where most of the students come to the class with little or no coding knowledge, and who would benefit from direct access to the class materials. To create this website, I used the openly licensed “Jupyter Books” application, which provides a basic template for adding text files in the Python programming language (used in the coursework)  directly to the website. Unlike most Content Management Systems such as WordPress, the Jupyter Books application allows students to see and access all of the class work, along with my annotations, and to download the Python files directly to their computers. Overall, it provides a useful resource for students to reinforce what they learn in class, and I hope that other instructors will take this work and build on it for similar classes. The website code can be downloaded under my github: https://github.com/gofilipa/fds-spring-23

Code” by Riebart is licensed under CC BY 2.0.

About the Author

Ingrid Conley-Abrams is an Adjunct Reference Librarian at the Mina Rees Library.