Introduction to Cultural Analytics & Python¶
This website hosts the course materials for “Introduction to Cultural Analytics: Data, Computation & Culture,” an undergraduate course taught at Cornell in the spring of 2020 and 2021. It also includes an online textbook designed for the class.
What is Cultural Analytics?
Cultural analytics is the study of culture with computational methods. Culture is a broad term that includes literature, history, politics, art, music, social media, and a lot more. Cultural analytics is a growing research area in fields like Digital Humanities and Information Science.
Python for Cultural Data
The Introduction to Cultural Analytics & Python textbook offers an introduction to the programming language Python that is specifically designed for humanities and social science students/scholars with no previous programming experience. It demonstrates how Python can be used to study cultural materials such as song lyrics, short stories, newspaper articles, tweets, Reddit posts, and film screenplays. It introduces computational methods such as web scraping, APIs, topic modeling, Named Entity Recognition (NER), network analysis, and mapping.
Most of the pages in the book are Jupyter notebook files that can be downloaded and run with Jupyter. Click the download button at the top of the page to download the .ipynb file. The Jupyter notebooks can also be opened and run in the cloud without any prior configuration by clicking the launch button . See How To Interact With This Book for more information.
Melanie Walsh, Introduction to Cultural Analytics & Python, Version 1 (2021), https://doi.org/10.5281/zenodo.4411250.
The DOI above links to a Zenodo archive of all evolving versions of the textbook. You may also link to the homepage https://melaniewalsh.github.io/Intro-Cultural-Analytics/, but be aware that it is subject to change.