Teaching
2021 to Present
I teach a free eight week data science bootcamp, mostly for high school students. I have offered the course 6 times to over 150 students. See here for a listing of the students’ final projects.
I have also taught an 11 week course involving an introduction to the Great Books.
Spring 2021 (at Harvard)
Gov 1005: Big Data
Syllabus and student evaluations
Everyone talks about big data. Few know how to deal with it. This course will teach you how to work with data of all sizes. How much money is donated to political campaigns? What characteristics are associated with voting Republican? Has the connection between income and ideology changed over time? We need data, often big data, to answer these questions.
This course, an introduction to data science, will teach you how to think with data, how to gather information from a variety of sources, how to import that information into a project, how to tidy and transform the variables and observations, how to visualize, how to model relationships, and how to communicate your findings. Each student will complete a final project, the first entry in their professional portfolio. Our main focus is data associated with political science, but we will also use examples from education, economics, public health, sociology, sports, finance, climate and any other social science topic which students find interesting.
Fall 2020
Gov 50: Data
Syllabus and student evaluations
This course, an introduction to data science, will teach you how to think with data, how to gather information from a variety of sources, how to import that information into a project, how to tidy and transform the variables and observations, how to visualize, how to model relationships, how to assess uncertainty, and how to communicate your findings. Each student will complete a final project, the first entry in their professional portfolio. Our main focus is data associated with political science, but we will also use examples from education, economics, public health, sociology, sports, finance, climate and any other topic which students find interesting.
Spring 2020
GOV 1005: Data
Syllabus and student evaluations
Data matters. Learning to think critically about data is a fundamental skill. How much money is donated to political campaigns? How do polls help us forecast elections? Does exposure to Spanish-speakers affect attitudes toward immigration? We need data to answer these questions – to describe, to predict, and to infer.
This course, an introduction to data science, will teach you how to think with data, how to gather information from a variety of sources, how to import that information into a project, how to tidy and transform the variables and observations, how to visualize, how to model relationships, how to assess uncertainty, and how to communicate your findings. Each student will complete a final project, the first entry in their professional portfolio. Our main focus is data associated with political science, but we will also use examples from education, economics, public health, sociology, sports, finance, climate and any other topic which students find interesting.
GOV 1006: Models
Syllabus and student evaluations
Statistical models help us to understand the world. This class explores the use of models for analysis in the social sciences broadly, and in political science specifically. Does a history of slavery in a county influence contemporary political views? Does perceived demographic change impact policy preferences? Does having daughters affect a judge’s rulings? We use the R programming language, RStudio, Git and GitHub. Each student will complete a replication as their final project, an attempt, successful or not, to reproduce the results from a published article in the academic literature. This class provides an introduction to data science and is designed to lay the groundwork for an empirical senior thesis.
Fall 2019
GOV 1005: Data
Syllabus and student evaluations
Data matters. Learning to think critically about data is a fundamental skill in the twenty-first century. How much money is donated to political campaigns? How do polls help us forecast elections? Does exposure to Spanish-speakers affect attitudes toward immigration? We need data to answer these questions – to describe, predict and infer.
This course, an introduction to data science, will teach you how to think with data, how to gather information from a variety of sources and in various formats, how to import that information into a project, how to tidy and transform the variables and observations, how to visualize the data, how to model relationships, how to assess uncertainty, and how to communicate your findings in a sophisticated fashion. Each student will complete a final project, the first entry in their professional portfolio. Our main focus is data associated with political science, but we will also use examples from education, economics, public health, sociology, sports, finance, climate and any other topic which students find interesting.
Spring 2019
GOV 1005: Data
Syllabus and student evaluations (numeric and written)
Data matters. The purpose of this course is to teach you how to work with data, how to gather information from a variety of sources and in various formats, how to import that information into a project, how to tidy and transform the variables and observations, how to visualize and model the data for both analysis and prediction, and how to communicate your findings in a sophisticated fashion. Each student will complete a final project, the first entry in their professional portfolio. Our main focus is data associated with political science but we use examples from social science more broadly.
GOV 1006: Models
Syllabus and student evaluations (numeric and written)
Statistical models help us to understand the world. This class explores the use of models for analysis in the social sciences broadly, and in political science specifically. Do get-out-the-vote calls affect turnout? Does a history of slavery in a county influence contemporary political views? Does demographic change impact policy preferences? We use the R programming language, RStudio, and GitHub. Each student will complete a “replication” as their final project, an attempt, successful or not, to replicate the results from a published article in the academic literature. This class is especially designed to lay the groundwork for an empirical senior thesis.
Fall 2018
GOV 1005: Data
Syllabus and student evaluations (numeric and written)
Data matters. How much money is spent on US political campaigns? How does the Chinese government use social media? How many seats will the Democrats control in the Senate after the next election? How does Harvard College decide whom to admit? We need data to answer these questions.
This course will teach you how to work with data, how to gather information from a variety of sources and in various formats, how to import that information into a project, how to tidy and transform the variables and observations, how to visualize and model the data for both analysis and prediction, and how to communicate your findings in a sophisticated fashion. Each student will complete a final project, the first entry in their professional portfolio. Our main focus is data associated with political science, but we will also use examples from education, economics, public health, sociology, sports, finance, climate and any other subject area which students find interesting.
We use the R programming language, RStudio, GitHub and DataCamp. Although we will learn how to program, this is not a course in computer science. Although we will learn how to find patterns in data, this is not a course in statistics. We focus on practice, not theory. We perform empirical analysis rather than write mathematical derivations. We make stuff.
Spring 2018
Ec 970: Sophomore Tutorial
Syllabus and student evaluations
Elite Education: Rhetoric and Empirics
This course reviews the economic literature on elite colleges in the United States while studying the theory and practice of persuasion: using words, statistics and graphics to convince others, and yourself, of some claim. You will learn how to write persuasive essays, how to pick out the flaws in your opponent’s argument, how to shift the terms of a debate to your advantage and how to marshal statistics and graphics to your side. We will study topics like: Which students choose to apply to colleges like Harvard? Which are admitted? What influence do athletics, legacy, race and wealth play in admissions? How do students perform while at college? How much of a problem is grade inflation and what might be done about it? Do elite colleges increase or decrease economic mobility? How generous are alumni after graduation? Yet beyond the empirical question concerning how the world works today, we are also interested in discussing the normative questions surrounding how elite colleges ought to function tomorrow. Natural Philosophers, the classical name for Economists, have wrestled with questions of Rhetoric since Plato confronted the Sophists two thousand years ago. Our class will continue that conversation in the context of contemporary debates about elite education in the United States.
Spring 2016 (at Middlebury)
INTD 0318 : Quantitative Finance
This class will introduce students to applied quantitative equity finance. First, we will develop the technical skills needed to do serious research, the most important of which is proficiency with R and RStudio. Second, we will briefly review the history and approach of academic research in equity pricing via selected readings. Students will work as teams to replicate the results of a published academic paper and then extend those results in a nontrivial manner. This course is designed for two types of students: first, those interested in applied financial research, and second, those curious about how that research is used and evaluated by finance professionals.
January 2009 (at Williams College)
ECON 18: Quantitative Equity Analysis
This class will introduce students to applied quantitative equity research. We will briefly review the history and approach of academic research in equity pricing via a reading of selected papers. Students will then learn the best software tools for conducting such research. Students will work as teams to replicate the results of a published academic paper and then extend those results in a non-trivial manner. This course is designed for two types of students: first, those interested in applied economic research, and second, those curious about how that research is used and evaluated by finance professionals.