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Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Welcome!
About me
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Posts
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
research
talks
teaching
Political Analysis [UG]
Recitation Section Leader, UG Methods, 2016
(with Scott Desposato) The course introduces undergraduate students to the logic of inference in social science, quantitative analysis in political science and public policy including research design, data collection, data visualizaion, and hypothesis testing. My most recent teaching evaluation is here.
Quantitative Methods I [G, x2]
Recitation Section Leader, Graduate Methods, 2019
(with Teevrat Garg) The course provides students with quantitative methods that are used for optimization and decision making. My most recent teaching evaluation is here.
Quantitative Methods II [G, x2]
Recitation Section Leader, Graduate Methods, 2019
(with Francisco Garfias and Jen Burney) The course is the first code intensive class that students encounter in the MIA/MPP program. It introduces linear models for policy analysis using Stata and R. My most recent teaching evaluation is here.
Quantitative Methods III [G]
Recitation Section Leader, Graduate Methods, 2019
(with Craig McIntosh) The course introduces causal inference, panel data analysis, and times series analysis to MIA/MPP students who are interested in quantitative analysis of business, policy, and economic development. My most recent teaching evaluation is here.
Machine Learning in Social Sciences [UG]
Instructor of Record, UG Methods, 2020
This course provides further development in Python, and develops advanced computational problem solving skills including common machine learning algorithms, data structures, and advanced tool and library options. My most recent teaching evaluation is here.
Social Data Analytics [UG, x5]
Instructor of Record, UG Methods, 2021
The course applies probability and statistical analysis for understanding data in the social world. Students engage in hands-on learning with applied social science problems. We cover the basics of probability, data visualization, data collection and management, hypothesis testing, and computation with Excel, Stata, and R. My most recent teaching evaluation is here.