Introduction to Cultural Analytics & Python

Designed by Melanie Walsh // Powered by Jupyter Book

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. It includes a short 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 students and 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.

Interactive Code
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.

This course was inspired by a range of excellent course materials, including those by Lauren Klein, David Mimno, and Allison Parrish.