Teaching

UT Austin: Gov 385L Gov 391J Gov 391K

UTokyo: QSS

Harvard: Gov 62 Gov 94jc  Gov 2000 Gov 2002 Gov 2003 Gov 2018 

Past and current graduate students: Students 

UT Austin

Gov 385L: Making Big Data

Level: Graduate

Taught: Spring 2023

Role: Instructor of Record

Description: In the social and natural 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 dissertation projects but also to the broader cumulation of knowledge in the social sciences. Undergraduates with the appropriate background are welcome with instructor permission.

[Syllabus]

Gov 391J: Statistical Analysis in Political Science I

Level: Graduate

Taught: Fall 2022, Fall 2023

Role: Instructor of Record

Teaching Assistants: 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.

[Syllabus]

Gov 391K: Machine Learning in Political Science

Level: Graduate

Taught: Fall 2022

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.

[Syllabus]

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.

Gov 62: Research Practice in Qualitative Methods

*Recipient, Certificate of Distinction in Teaching Award*

Level: Undergraduate

Taught: Spring 2018

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.

Gov 94jc: Making Big Data

Level: Undergraduate

Taught: Spring 2024

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.

[Syllabus]

[Piazza]

Gov 2000: Introduction to Quantitative Methods I

*Recipient, Certificate of Distinction in Teaching Award*

Level: Graduate

Taught: Fall 2018

Role: Teaching Fellow

Instructor of Record: Xiang Zhou

Description: An introduction to statistical research in political science with a focus on applied linear regression.

Gov 2002: Topics in Advanced Quantitative Methodology

Level: Graduate

Taught: Fall 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.

Gov 2003: Topics in Quantitative Methods

*Recipient, Certificate of Distinction in Teaching Award*

Level: Graduate

Taught: Spring 2019

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.

Gov 2018: Introduction to Machine Learning

Level: Graduate

Taught: Spring 2024

Role: Co-instructor of Record with Naijia Liu

Teaching Fellow: 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.

[Syllabus]

[Course Website]

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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