Reviewed by:
Christy Anderson, Marketing/Administrative Assistant
Kimbel Library and Bryan Information Commons, Coastal Carolina University
https://doi.org/10.17613/ep1m-6583
The digital book Introduction to Cultural Analytics & Python has already become a recommended resource within the field of digital humanities. Though it was created as a textbook for a particular class at Cornell University, this new resource is so comprehensive that it can be utilized beyond the context of the course it was created for. The book covers a lot of ground, from Python basics and how to use Jupyter to curating data and running analysis.
Written by Melanie Walsh while she was a Postdoctoral Associate in Information Science at Cornell, Introduction to Cultural Analytics & Python was designed to accompany an undergraduate course by the same name. According to Walsh’s bio on Humanities Commons, the book was created to prepare “students to analyze cultural materials — such as books, movies, historical records, and social media posts — with digital and computational tools.” Walsh explains cultural analytics as a mix of computers and human life that applies computational methods to the study of culture. Cultural analytics is a growing discipline that stems from fields like Digital Humanities and Information Science.
This “how to” guide is as direct as they come and embodies its ethos as an open educational resource (OER). The site’s table of contents runs down the right side, while the book’s contents are laid out along the left, listing each chapter with its sectional topics in a drop-down box. Step-by-step text is accompanied by embedded video tutorials throughout. Style wise, Introduction to Cultural Analytics & Python forgoes sleek graphics for design that is clean and simple. In keeping with open educational resources, the book is hosted on GitHub and powered by Jupyter Book, an open source project for building books. It is similar in look and feel to the European Holocaust Research Infrastructure open course Cultural Analytics: Interactive Learning Environment in R, which utilizes Swirl software to teach R language.
This resource is definitely created for an extremely specific audience. While it is an introductory text, it is not a fully explanatory one. For those with only a vague understanding of programming and data analysis, the content may feel foreign. For a reader with that foundational understanding, however, this book will be incredibly useful in fostering practical understanding. The book gives learners hands-on experience by walking them step-by-step through the interactive process . It also houses a growing repository of datasets related to culture and humanities with examples of how they can be used.
There are several ways to go about using Introduction to Cultural Analytics & Python, including PDF download or in the Cloud. However, to fully engage with the pages, Jupyter Notebook needs to be downloaded. It is also important to note that there is a good deal of hidden content in the book. By clicking on the embedded “Click to show” found on most pages, varying code, answers to practice questions, and extra activities will be revealed. Walsh designed this hidden content “to replicate the experience of running code live.”
There is not anything quite like Introduction to Cultural Analytics & Python openly available on the web. There are other lessons and courses that focus on individual components, such as text analysis, Python, Pandas, and machine learning However, Walsh’s work combines these components for the specific task of cultural analysis in a way that is fully interactive and highly educational. Walsh packs in a lot of “how to” skills with basic understanding of the topics. In essence, this book is the go-to for taking your knowledge of digital humanities and putting it into practical application.