The course introduces social scientists to computational techniques for analyzing large-scale textual information. In an era where vast amounts of text—from policy speeches and legislative documents to social media and news archives—shape our understanding of the world, this course equips students with the methodological tools to extract meaningful insights from unstructured data. Bridging natural language processing (NLP), machine learning, Bayesian statistics, and the social sciences, the course focuses on practical applications in areas like political discourse analysis, sentiment detection, and policy communication. Students will learn essential preprocessing techniques (e.g., tokenization, stemming, stopword removal), text representation methods (bag-of-words, word embeddings), and advanced modeling approaches, including supervised learning, topic modeling, and deep learning techniques such as BERT and large language models (LLMs) like OpenAI and LLAMA. By integrating hands-on coding exercises with theoretical discussions, the course prepares students to engage with textual data critically, ensuring they can apply these techniques to real-world social science research while considering the ethical and methodological challenges of working with text-based data.

How can we measure income inequality? What factors predict election outcomes? Do policy interventions effectively reduce poverty? Social scientists, policymakers, businesses, and non-profit organizations increasingly rely on data to answer these pressing questions—using it to describe trends, predict outcomes, and inform decisions. This course equips students with the tools to analyze data, draw statistical inferences, and apply causal reasoning to real-world political, social, and economic issues. Through hands-on experience with quantitative methods used in academic research, policy analysis, and the private sector, students will gain essential data skills—no prior statistical knowledge is required.

How do political and economic forces shape global trade? What are the key theories explaining trade patterns, and how do they apply to modern challenges like climate change and automation? This course explores the political economy of international trade, tracing foundational theories and debates while examining contemporary issues. Students will engage with classic and modern perspectives to understand how trade policies are shaped, who benefits from them, and how global economic shifts influence political decision-making.

The MPE Summer Program is an eight-week, in-residence research and mentoring initiative funded by the National Science Foundation (NSF) through a Research Experience for Undergraduates (REU) award (Award No. #2150250). Designed to support underrepresented and first-generation undergraduate students, the program prioritizes recruiting from Minority Serving Institutions (MSIs). It enhances academic success through rigorous methodological training and structured guidance for graduate school applications. With an interdisciplinary approach, MPE equips students with the analytical tools to examine key issues in mobilization and political economy. By integrating theory with hands-on data analysis, participants develop essential research skills to tackle pressing social, political, and policy challenges, bridging the gap between academia and real-world applications.

The Quantitative Methods Course lays a strong foundation in research design, descriptive statistics, and regression analysis before advancing to complex techniques like hypothesis testing and multi-level modeling (MLM). These methods are particularly powerful in examining structural inequalities, allowing students to analyze the interplay between individual behaviors and systemic factors. A key component of the course is hands-on training in R programming, enabling participants to work with real-world datasets, such as the Collaborative Multi-racial Post-election Survey (CMPS) and census data. The program provides students with essential analytical skills for academia, government, and private industry by combining methodological rigor with a focus on diversity and inclusion. Students gain valuable experience in data management, statistical modeling, and policy analysis—preparing them for a world increasingly driven by data science.

The course aims to provide students with the ability to understand, explain, and perform political science quantitative research. The focus will be on data analysis, and students will learn how to draw statistical inferences and causal reasoning. No previous statistical knowledge is required for this course. After taking this course, students will be able to read and understand the methodology used by social media reports, policy memos, and most academic articles in political science. This course will represent an essential skill for students as the first step into a world that is increasingly dependent on data science.