Practical Approaches to Data Science with Text

Emory University / QTM 340 / Fall 2021

What does it mean to turn text into data? What are the data science techniques that are commonly employed in order to analyze text? How are they applied in the humanities and social sciences? How are they applied in the world? This course explores these questions by focusing on how existing methods of text analysis can be used in new and creative ways. These methods include text parsing, natural language processing, language models, and vector space models, as well as statistical approaches including cluster analysis and supervised and unsupervised learning. We will also discuss contemporary topics including data ethics, data justice, and issues with “humans in the loop.”

Introductory courses in computer science and probability and statistics are recommended as perquisites for this course. You will complete all class exercises and homework assignments in Python. I expect you to participate in class discussion and present your final project at the end of the semester. I will also require some short writing assignments.

• Syllabus
• Course Calendar
• Course on Canvas (Emory students only)
• Email me!