Since 2016, Connor Jerzak has taught across 19 course offerings at UT Austin, Harvard, and the University of Tokyo: 14 as instructor or co-instructor of record and five as a teaching fellow or teaching assistant (across these courses, a 300+ student enrollments).
UT Austin: Gov 355M Gov 385L Gov 391J Gov 391K
Harvard: Gov 62 Gov 94jc Gov 2000 Gov 2002 Gov 2003 Gov 2018
UTokyo: QSS
Students, Past & Present: Students
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UT Austin
Gov 355M: AI & Programming Foundations for Computational Social Science
Level: Undergraduate
Taught: S2026
Role: Instructor of Record
Teaching Assistants: Gauthier Fally (2026)
Description: In an era where data and AI drive decision-making in policy, government, and social sciences, this hands-on introductory course equips students with essential programming and computational skills to analyze complex social phenomena. Students will learn foundational programming in R, SQL, and Python, as well as how to find, collect, and clean information from open data portals, APIs, and the web using AI agents. Along the way, we emphasize reproducibility (notebooks, version control), clear communication of data quality (tables, maps, and figures), and core principles behind trustworthy programming and data analysis in the AI age. Through hands-on projects involving web scraping, data visualization, and designing/deploying autonomous AI agents, participants will gain practical experience to accelerate research and problem-solving.
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Gov 385L: Making Big Data
Level: Graduate
Taught: S2026, S2025, S2023
Role: Instructor of Record
Description: In the social sciences, quantitative analyses depend critically on the availability of high-quality data, but these data are rarely readily available at the click of a mouse. Instead, they are often messy, in difficult formats, or in forms not immediately useable in statistical software programs. In this class, we will learn how to find, curate, and create high-quality social science data. We will balance the teaching of practical tools—such as web scraping, crowd-sourcing techniques, online surveys, massive database management, and dataset merging—with the examination of theoretical perspectives on the role of measurement error in the analysis of causality. Code will also be treated as a form of data, and some teaching emphasis placed on how to make software packages that can improve reproducibility and enhance research impact. Projects completed during the course will give students an opportunity to create new, high-quality data contributing not only to their research projects but also to the broader cumulation of knowledge in the social sciences. Geared towards graduate students with some coding background in R or Python. Undergraduates with appropriate background are welcome (instructor permission needed).
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Gov 385L: Causal ML & EO for Social Sci
Level: Graduate
Taught: F2026
Role: Instructor of Record
Description: This course introduces students to cutting-edge methods in causal machine learning and Earth observation (EO) for the social sciences. Drawing on Planetary Causal Inference research, participants will learn how satellite imagery and large-scale remote sensing data can reveal new dimensions of treatment effect heterogeneity, confounder modeling, and outcome measurement. Through a blend of methodological foundations and applied exercises, the course examines how these tools enable researchers to understand the transportability and generalizability of causal effects across diverse geographic and socioeconomic contexts. Students will gain hands-on experience integrating EO data into experimental and observational frameworks—such as randomized controlled trials (RCTs) and natural experiments—to strengthen causal claims on topics ranging from environmental policy to economic development. By the end, participants will be equipped to leverage planetary-scale data and state-of-the-art causal ML techniques to address pressing questions at the intersection of society, economy, and the environment.
[Syllabus TBA] [Canvas Link]
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Gov 391J: Statistical Analysis in Political Science I
Level: Graduate
Taught: F2026, F2023, F2022
Role: Instructor of Record
Teaching Assistants: Elle Diether (2026), Andrés Cruz (2023), Matthew Martin (2022)
Description: Social scientists seek to understand the social world, and this understanding is often critically based on the quantitative analysis of data. In this course, we introduce students to this mode of inquiry. We will explore classical approaches for bringing evidence to bear for empirical claims, examining their strengths, limitations, theoretical properties, and manner of implementation. Modeling strategies introduced will include probability models, linear regression, and some introductory causal inference approaches. Emphasis will also be placed on the effective communication of quantitative evidence. Therefore, we will emphasize throughout the course the importance of visualization methods, placebo tests, model robustness checks, and the qualitative meaning of the quantitative assumptions employed by various modeling strategies. By the end of the course, students should be able to tackle challenging empirical problems in their research with a variety of modeling strategies and to effectively communicate the implications of their analyses to their target audience.
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Gov 391K: Machine Learning in Political Science
Level: Graduate
Taught: S2025, F2022
Role: Instructor of Record
Teaching Assistants: Siyun Jiang (2022)
Description: In this course, we will explore the universe of machine learning (ML) modeling strategies—their motivation, properties, and manner of implementation. Models examined will include tree-based methods, convolutional network networks, machine learning approaches for social networks, time series network models, ensembles, and probabilistic machine learning. We will interrogate how the use of these strategies can be used to support social science inquiry. We will also examine topics explicitly at the intersection of social science and ML itself—topics related to fairness, privacy, and the implications of ML for politics and society. By the end of the course, students should be able to employ state-of-art machine learning methods in their research and policy applications, understand the strengths and limitations of these methods, and know how to modify the proposed methods to maintain legal compliance, privacy, and ethics.
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Gov 62: Research Practice in Qualitative Methods
*Recipient, Certificate of Distinction in Teaching Award*
Level: Undergraduate
Taught: S2018
Role: Teaching Fellow
Instructor of Record: Frances Hagopian
Description: The primary objective of this seminar is to introduce students to the basic principles and tools of qualitative research in the social sciences. A second objective of the course is to prepare students to undertake original research for their senior thesis projects. The course therefore focuses on issues of qualitative research design and methodological application in comparative social science.
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Level: Undergraduate
Taught: S2024
Role: Instructor of Record
Description: In the social sciences, analyses depend on the availability of high-quality data, but these data are rarely available at the click of a mouse. Instead, they are often messy or in inaccessible formats. In this seminar, we will teach how to find, curate, and create high-quality data. We will balance the teaching of practical tools—e.g., webscraping and massive database management—with theory regarding measurement error. The class concludes with projects generating new, high-quality data. To enhance project impact, code will also be treated as a form of data, with instruction on software development. Geared towards undergraduates with R experience.
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Gov 2000: Introduction to Quantitative Methods I
*Recipient, Certificate of Distinction in Teaching Award*
Level: Graduate
Taught: F2018
Role: Teaching Fellow
Instructor of Record: Xiang Zhou
Description: An introduction to statistical research in political science with a focus on applied linear regression.
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Gov 2002: Topics in Advanced Quantitative Methodology
Level: Graduate
Taught: F`2016
Role: Teaching Fellow
Instructor of Record: Michael Peress
Description: Graduate course on the theory and implementation of causal inference methods for social science research. Topics include randomized experiments, matching, diff-in-diff, instrumental variables, regression discontinuity designs, sensitivity analysis, and causal mediation.
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Gov 2003: Topics in Quantitative Methods
*Recipient, Certificate of Distinction in Teaching Award*
Level: Graduate
Taught: S2019
Role: Teaching Fellow
Instructors of Record: Matthew Blackwell and Kosuke Imai
Description: This course covers topics of general interest to political methodology: causal inference, graphical models, mixed methods, contest modeling, text-as-data, item response. Illustrates how ideas and methods from these areas can be applied to substantive questions.
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Gov 2018: Introduction to Machine Learning
Level: Graduate
Taught: S2024
Role: Co-instructor of Record with Naijia Liu
Teaching Fellows: Yuning Liu & Ruofan Ma
Description: This course serves as a graduate-level introduction to machine/statistical learning for social scientists. It will cover some, but not all, common techniques to collect, analyze and utilize large and unstructured data for social science questions. The general goal of this course is to introduce students to modern machine learning techniques and provide the skills necessary to apply the methods widely.
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Math Prefresher: A Short Course in Quantitative Methods
Level: Graduate [Course Website]
Taught: Summer 2016, Summer 2017
Role: Instructor of Record
Description: A two-week graduate course on mathematics and computer programming for social scientists. The course introduces the mathematics and computer skills needed for quantitative and formal modeling courses offered at Harvard.
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University of Tokyo
Introduction to Quantitative Social Science
Level: Undergraduate
Taught: Summer 2021
Role: Teaching Assistant
Instructor of Record: Kosuke Imai
Description: The course introduces basic principles of statistical inference and programming skills for data analysis. The goal is to provide students with the foundation necessary to analyze data in their own research and to become critical consumers of statistical claims made in the news media, in policy reports, and in academic research.
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