This course will develop the skills to prepare, analyze, and present data for policy analysis and program evaluation using R. In Quant I and II, students are introduced to probability and statistics, regression analysis and causal inference. In this course we focus on the practical application of these skills to explore data and policy questions on your own. The goal is to help students become effective analysts and policy researchers: given the available data, what sort of analysis would best inform our policy questions? How do we prepare data and implement statistical methods using R? How can we begin to draw conclusions about the causal effects of policies, not just correlation? What should we keep in mind to make sure we’re using “data for good”, especially when the focus is on marginalized communities using data on personal identity?

 

Please review the most recent syllabus before completing this application. 

The purpose of this registration form is to help manage the waitlist and make sure that all admitted students are prepared to succeed, which means: (1) understanding and satisfying the DataCamp prerequisites before the start of the semester, if applicable; (2) understanding the applied microeconometrics focus of the course; and (3) committing to completing the weekly course responsibilities, including group data project work during the latter half of the semester. You should not be deterred if you are brand new to R or found Quant II challenging, as long as you are committed to putting in the work.

Pre-req: INAF U6072 - Energy Systems Fundamentals. Artificial intelligence (AI) is a hot topic. Over 200 million people now use ChatGPT each month, tens of billions of dollars have poured into AI projects over the past year, and policymakers around the world are considering how best to respond to AI’s rapid growth.

At the same time, countries are grappling with the urgent challenge of climate change. Based on global average temperatures, July 22, 2024, was the warmest day ever recorded; 2023 was the warmest year ever recorded; and the 10 warmest years on record are the last 10 years. Despite encouraging developments, such as the dramatic drop in renewable energy costs over the past decade, global greenhouse gas emissions continue to rise.

Can AI help reduce greenhouse gas emissions? Will the increased power demand for AI result in more emissions, offsetting any benefits? Can AI aid in climate change adaptation? Should policymakers encourage AI use to combat climate change while discouraging AI applications that may increase emissions? If so, how?

This advanced seminar will explore these questions. After an introductory session on core AI concepts, we will examine how AI could reduce emissions and aid adaptation to climate change, as well as the ways AI could contribute to increased emissions. We will discuss barriers to using AI in climate action, risks associated with AI in this context, policy options to address these risks and barriers, and strategies for stakeholders to collaborate in leveraging AI tools to combat climate change.

As far back as the Revolutionary War, American citizens have been engaged in secret intelligence operations in wartime. Over the past eighty years, the US government has made secret intelligence and covert operations a regular part of its toolkit for dealing with foreign challenges or domestic threats in peacetime as well as wartime. Reconciling these secret activities and the institutions and individuals responsible for them with a democratic Republic based on checks and balances and electoral accountability has been a ceaseless work-in-progress, whose tempo increases or decreases depending on world and domestic events.

Regardless of the tempo, however, the overarching question is unchanged: Can secret intelligence activities–including lying to deny or mask the government’s involvement–be reconciled with American democracy? This seminar examines the law, policy, and history of U.S. intelligence activities. It explores such issues as the constitutional allocations of power for intelligence, the evolution of American intelligence organizations over time, dilemmas created by new surveillance technologies and ways to address them (or not), congressional oversight of covert action–including lethal force–in which the U.S. government intends to hide its hand, and the roles of courts and the press as checks.

This is a joint offering between SIPA and the Law School, because the history, policy, and law of American intelligence activities are so intertwined. To understand the future in this area, one must understand those interconnections.

 

Was the pro-life narrative strategy a decisive factor in overturning Roe v. Wade? After countless videos of police brutality, why did the video of George Floyd’s murder dramatically accelerate the pace of cultural and policy change? After years of campaigns to reduce teen pregnancy, how was it that a TV show became one of the main drivers of reducing teen pregnancy to the lowest point in recorded history? After losing 31 state referendums, why did a new narrative approach enable the gay marriage campaign to start winning nationwide? These questions and storytelling examples are part of broader social impact campaigns which combined the right mix of strategy and narrative to create change. A social impact campaign is one that creates a significant change that addresses a pressing social issue. Often, there is too little focus on the power of narrative to change behavior and drive action.

 

This class will explore all aspects of social impact campaigns that harness the power of “effective” stories to engage audiences and prompt action. Additionally, we will investigate how corporations and brands develop campaigns and how they partner with the government, foundations and NGOs. Students will have the chance to question some of the leading creators/practitioners as they create their own social impact campaigns.

 

 

Prerequisites: Students should have taken, at minimum, an introductory class in statistics before taking this course. Basic knowledge of calculus, probability, distributions, hypothesis testing, and linear models will be essential for understanding the concepts discussed in class. In addition, students should be able to work with the R programming language – it will be the core language and software environment in this course. Without familiarity with R, the course will be very challenging.

This course is an introduction to the quantitative analysis of text as data – a rapidly growing field within the social sciences. The availability of textual data has grown massively in recent years, and so has the demand for skills to analyze it. Vast amounts of digital content are becoming increasingly relevant to various policy-relevant questions. For example, social media data are now commonly used to understand public opinion, engagement with politics, behavior during natural disasters, and even pathways to extremism; candidates’ statements and rhetoric during elections are useful for estimating policy positions; and large amounts of text from news sources are used to document and understand world events.

While the wealth of information in text data is incredible, its sheer size makes it challenging to summarize and interpret without quantitative methods. In this course, we will learn how to quantitatively analyze text from a social-science perspective. Throughout the course, students will learn different methods to acquire text, how to transform it to data, and how to analyze it to shed light on important research questions. Each week we will cover different methods, including dictionary construction and application, sentiment analysis, scaling and topic models, and machine learning classification of text. Lectures will be accompanied by hands-on exercises that will give students practical experience while working with real-world texts. By the end of the course, students will develop and write their own research projects using text as data.

 

Nuclear weapons have not been used on enemy targets since 1945, but they have been deployed, detonated, threatened, and threatened many times to affect the behavior of others. What can we learn from past experiences that can help us prepare for or avoid future contingencies? The proposed mini-course would meet five times in person for three hours each time, with readings and videos in preparation for each in-person meeting.

 

This course will bridge the gap between data science and public policy in several exciting ways. By drawing on a diverse student body – consisting of students from SIPA, the Data Science Institute, the Quantitative Methods in the Social Sciences, the Statistics and Computer Science programs – we will combine domain-level policy expertise with quantitative analytical skills as we work on cutting-edge policy problems with large amounts of data.

Throughout the semester, students will have the opportunity to analyze and examine real-world datasets on a broad range of policy topics, including, for example, data on disinformation campaigns on social media, data relating to privacy and computer vision, and granular information on natural disasters that can facilitate preparedness for future hazards.  In addition, students will work in interdisciplinary policy – data science teams on semester-long projects that develop solutions to policy problems drawing on big data sources. By the end of the course, students will gain hands-on experience working with various types of data in an interdisciplinary environment – a setting that is becoming more and more common in the policy world these days.

 

 

This course explores skills needed for sophisticated spreadsheet development and problem solving in Microsoft Excel. Topics include implementing advanced logic using complex formulas, managing complexity with Excel's auditing features, leveraging lookup functions leveraging and calculated references, parsing and cleaning raw data, refining data structure, and constructing and leveraging PivotTables. The course does not focus on specific models or applications, but instead explores general concepts and techniques that can be flexibly applied to different solutions in Excel. 

Prerequisite 2: 

Students are expected to have some basic exposure to R and the Tidyverse package in particular, or a demonstrated aptitude for object-oriented programming languages. We love brand new R users, we just ask that you do a bit of preparation before our first class.

Personal interest in the course