Quantitative Methods in the Social Sciences

This course is meant to provide an introduction to regression and applied statistics for the social sciences, with a strong emphasis on utilizing the Python software language to perform the key tasks in the data analysis workflow. Topics to be covered include various data structures, basic descriptive statistics, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, data visualization, models for binary outcomes, models for ordered data, first difference analysis, factor analysis, and cluster analysis. Through a variety of lab assignments, students will be able to generate and interpret quantitative data in helpful and provocative ways. Only relatively basic mathematics skills are assumed, but some more advanced math will be introduced as needed. A previous introductory statistics course that includes linear regression is helpful, but not required.

Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.

Artificial intelligence (AI) and generative AI – like ChatGPT, MidJourney, and Gemini – are poised to change the world for everyone. It is critical that students understand (and utilize) this new technology at several levels. In this class – through readings and a dozen hands-on activities – students will come to deeply understand AI. Specifically, students will construct (using Python) some of the basic building-blocks of AI, like machine learning (like recommendation systems), natural language processing (like word embeddings) and chatbots. They will test out AI’s capabilities and refine prompts in real-world settings, whether in art, video, writing or Internet-of-Things. They will learn about how generative AI fits into the history of technology adoption and the diffusion of innovation, answering questions like: Will AI be able to replace whole jobs? And if so, when? They will use the lenses of psychology and economics to explore the impact of AI in people’s lives, including in the context of algorithmic fairness, regulation and intellectual property. They will be pushed to take human creativity in new directions, augmented by AI’s “weirdness.” Lastly, students will be pushed to further develop their own uniquely-human skills – like in critical thinking and empathy – in response to the power of generative AI to mimic humans. As best-selling author Seth Stephen-Davidowitz has recently (Dec. 2023) written, “So far, [my newest book] has higher ratings than either of my previous two books -- even though it was written in 1/36th of the time, thanks to AI. AI is wild!" By the end of this class, students will feel empowered technically and philosophically to handle all new generative AI developments. There are no specific prerequisites for this class.

This course offers students an opportunity to expand their curriculum beyond the established course offerings. Interested parties must consult with the QMSS Program Director before adding the class. This course may be taken for 2-4 points.