Teaching Assistant for Undergraduate Courses at New York University
- Political Theory (core course, POL-UA 100): Fall 2017, Fall 2015
Instructor: Dimitri Landa
Description: The course provides an introduction to political theory through the analysis of seminal historical and contemporary texts. It focuses on the critical issues of the distinct nature of public morality, the structure and defense of liberty, equality and justice, and the different models of democratic politics. The readings include selections from the works of Plato, Aristotle, Machiavelli, Adam Smith, Hobbes, Locke, Rousseau, Madison, Mill, Marx, and Rawls.
- Power and Politics in America (core course, POL-UA 300): Fall 2013
Instructor: Patrick Egan
Description: An introductory course on the national politics of the United States. The course includes discussions of America’s political institutions and political culture, and examines how elections and political parties, in combination with strong interest groups, produce the type of representative democracy observed in the United States. In addition, it will place American politics in a comparative perspective. The course also introduces students to the analytical and empirical tools that political scientists use to describe, explain and understand politics and public affairs.
- Quantitative Methods in Political Science (POL-UA 800): Fall 2016
Instructor: Arthur Spirling
Description: The course provides students with an introduction to probability theory, research design, and statistics with a view to testing hypotheses about politics. Students will learn a “toolbox” of methods — including statistical software — to organize and analyze data.
- Quantitative Methods in Political Science (POL-UA 800): Spring 2016, Spring 2015, Spring 2014
Instructor: Gizem Acikgoz
Description: This class is an introduction to the logic and the mathematics of statistics, with an emphasis on social-science applications. Topics include basic descriptive statistics, the logic of causal order, bivariate regression, multiple regression analysis, probability, confidence intervals, significance testing, regression diagnostics, multicollinearity and heteroskedasticity. Lab sessions include training in the computer application Stata.