Data Science and Text Analysis

Course Overview

The abundant textual data and advances in natural language processing (NLP) and machine learning give unprecedented opportunities and insights to answer fundamental social science questions. This course provides an introduction to data science for social data and problems, with a focus on utilizing text as data. Students will learn through hands-on projects the fundamental tools of data science and apply them to a wide range of political and policy-oriented questions, including scientific computing, text vectorization, visualization, unsupervised and supervised learning, and large language models. For each topic/method, we will also discuss published applications in the social sciences. Ideally, students in this class should have prior knowledge of research design and statistics at introductory to intermediate levels. While some experience with programming would make the course less challenging, it is NOT required.

By the end of the class, students will have a good understanding of the general landscape and frontier of text analysis and machine learning methods. Students should be able to identify and conduct the appropriate methods for their research questions.