Python for Public Policy

MIA and MPA Policy Skills II Core. 
This 7-week mini course exposes the students to the application and use of Python for data analytics in public policy setting. The course teaches introductory technical programming skills that allow students to learn Python and apply code on pertinent public policy data. The majority of the class content will utilize the New York City 311 Service Requests dataset. It’s a rich dataset that can be explored from many angles relevant to real-world public policy and program management responsibilities.

• Instructor: Aidan Feldman
  Info
• 1.5 points

Python for Public Policy

MIA and MPA Policy Skills II Core. 
This 7-week mini course exposes the students to the application and use of Python for data analytics in public policy setting. The course teaches introductory technical programming skills that allow students to learn Python and apply code on pertinent public policy data. The majority of the class content will utilize the New York City 311 Service Requests dataset. It’s a rich dataset that can be explored from many angles relevant to real-world public policy and program management responsibilities.

• Instructor: Aidan Feldman
  Info
• 1.5 points

Data Analytics for Public Policy, Admin

Intro to Data Analytics

MIA and MPA Policy Skills II Core. 
This course provides a practical introduction to the core concepts, techniques, and tools used to analyze data for effective decision-making. Designed for students with little to no background in statistics, mathematics, or statistical software, the course emphasizes intuitive understanding and hands-on learning. Through interactive exercises and real-world datasets, students will explore both qualitative and quantitative methods for extracting insights, identifying patterns, and building evidence-based recommendations. The course focuses on developing analytical reasoning and applied skills that can be used across a range of policy and professional contexts.

• Instructor: Poranee Kingpetcharat
  Info
• 1.5 points

Database Design, Management, and Securit

Database Design Mgmt & Se

MIA and MPA Policy Skills II Core. 
This advanced course provides a comprehensive introduction to the principles and practices of effective database design, management, and security. Students will gain a strong foundation in information organization, data storage, and database administration, with attention to key topics such as data warehousing, governance, security, privacy, and alternative database models.

The course emphasizes the relational database model and includes practical instruction in Structured Query Language (SQL), data modeling, and integrity constraints. Students will learn to design, build, and manage databases while addressing contemporary issues in security and privacy. Prior experience with basic programming and data structures is recommended.

• Instructor: Poranee Kingpetcharat
  Info
• 1.5 points

Data Collection for Evaluation, Policy,

MIA and MPA Policy Skills II Core. Pre-req: Quant I (SIPA IA6500).
 Research is an important part of the policy process: it can inform the development of programs and policies so they are responsive to community needs, reveal the impacts of these programs and policies, and help us better understand populations or social phenomena. This half-semester course serves as an introduction to how to ethically collect data for smaller research projects, with an in-depth look at focus groups and surveys as data collection tools. We will also learn about issues related to measurement and sampling. Students will create their own focus group protocol and short survey instrument designed to answer a research question of interest to them.

• Instructor: Rachel Swaner
  Info
• 1.5 points

R For Public Policy

MIA and MPA Policy Skills II Core. 
This 7-week mini-course leads the students into the R world, helps them master the basics, and establishes a platform for future self-study. The course offers students basic programming knowledge and effective data analysis skills in R in the context of public policy-making and policy evaluation. Students will learn how to install R and RStudio, understand and use R data objects, and become familiar with base R and several statistical and graphing packages. The course will also emphasize use cases for R in public policy domains, focusing on cleaning, exploring, and analyzing data.

• Instructor: Mike Q Zhu
  Info
• 1.5 points

Introduction to Infographics and Data Vi

Intro to Data Visualizati

MIA and MPA Policy Skills II Core. 
This course introduces students to the principles and practices of data visualization as a powerful tool for interpreting and communicating complex information. As large datasets become increasingly available across sectors, the ability to transform raw data into clear, compelling visuals is essential for insight and decision-making.

Students will learn to select appropriate visualization types, apply design techniques that balance form and function, and tell analytic stories with clarity and impact. Through hands-on assignments and guided case studies, the course builds practical skills in visualizing data to uncover patterns, reveal trends, and engage diverse audiences.

• Instructor: Douglas Williamson
  Info
• 1.5 points

Artificial Intelligence in Public Policy

Artificial Intel in Publi

MIA and MPA Policy Skills II Core. Pre-req: Computing in Context (DSPC IA6000). 
This course introduces students to the fundamentals of Artificial Intelligence (AI), its applications in public policy, and its implications for the future of governance. Students will gain a foundational understanding of AI, including the mathematical and programming principles behind common machine learning algorithms used for prediction, classification, and clustering. The course explores the practical applications of AI across various sectors, including business, non-profits, and government, highlighting its transformative potential. In the final segment, students will apply their knowledge to design AI solutions for public policy challenges. Through a "Concept to Implementation" process, student groups will identify problems, navigate data and algorithmic considerations, and propose actionable AI-driven solutions.

• Instructor: Sameer R Maskey
  Info
• 1.5 points

Building AI Tools with Large Language Mo

MIA and MPA Policy Skills II Core. Pre-reqs: Working Python knowledge OR Python for Public Policy (SIPA IA6650) OR Intro to Text Analysis in Python (SIPA IA6655). 
In the past two years, Large Language Models (LLMs) built using transformer frameworks have emerged as the fastest-growing area of research and investment in AI/machine learning. Recent releases of chatbots such as ChatGPT (OpenAI), Bing (Microsoft), and Bard (Google) quickly reached hundreds of millions of users and have become the face of artificial intelligence for consumers. There has also been an explosion in the number of applications that depend on LLMs for a variety of more specialized tasks. Recent models have shown impressive performance on both canonical machine learning tasks and for everyday use, yet are in many ways poorly understood and, in some cases, exhibit unexpected and potentially harmful behavior. 

Policymakers, analysts, and non-profit and industry leaders need an understanding of these models to take advantage of the opportunities they present and to mitigate potential harms. This course provides an overview of Large Language Models and gives students hands-on experience with various ways of interacting with LLMs. Students will learn to interpret model evaluation metrics, and we will discuss safety and ethics in applied contexts.

• Instructor: Mike Q Zhu
  Info
• 1.5 points

Data Analysis for Inclusive Policy

Data Analysis-Inclusive P

MIA and MPA Policy Skills II Core. Pre-req: Quant I (SIPA IA6500)
. Data is not neutral. How it is collected, categorized, and analyzed is shaped by historical, political, economic, and social forces, often reinforcing existing injustices. While policy professionals are trained in quantitative methods, there is comparatively less focus on interrogating how data itself is produced, how existing frameworks exclude certain populations, and how data can be used to either reinforce or challenge inequities.

This course introduces students to inclusive and decolonial approaches to working with data for policy research and advocacy, emphasizing critical engagement with its lifecycle—from collection to analysis to dissemination. Students will examine how statistical tools, methods, and available data can be utilized to either reinforce or dismantle barriers to opportunity and address structural injustices. Through weekly discussions, hands-on coding exercises, and a research or advocacy project sketch, students will examine a range of data sources and methodologies while developing strategies for ethical, community-centered data practices.

• Instructor: Harold B Stolper
  Info
• 1.5 points

Applying Machine Learning

The widespread adoption of information technology has resulted in the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand and improve the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and social science (theory, reduced-form causal inference, structural modeling) to address real-world problems. We will use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Pre-requisites: 
Quantitative Analysis II, Microeconomics, and an introductory computer science course (DSPC IA6000 or equiv). Students who have attained mastery of the prerequisite concepts through other means may petition for an exception to the prerequisites using the form: 
https://bit.ly/applyingMLpetition

• Instructor: Daniel C Bjorkegren
  Info
• 3 points

Data Science and Public Policy

Pre-req: SIPA IA6501 - Quant II
or equivalent quantitative methods course. This course bridges the gap between data science and public policy by bringing together students from diverse academic backgrounds to address contemporary policy challenges using large-scale data. With the rapid growth of digital information and the increasing influence of machine learning and AI on public life, the ability to work across disciplines is becoming essential.

Students will examine real-world datasets on topics such as disinformation campaigns, privacy and surveillance, crime and recidivism, natural disasters, and the impact of generative AI. Through weekly presentations and a semester-long team project, students will gain practical experience applying data science methods to pressing policy issues while learning how to collaborate across fields.

• Instructor: Tamar Mitts
  h-index: 5 Info
• 3 points

Text as Data

Pre-req: SIPA IA6500 - Quant I, and prior experience with R are required.
 This course introduces students to the quantitative analysis of text, an increasingly important method in the social sciences and public policy. With vast amounts of textual information now available from sources such as social media, news articles, political speeches, and government documents, the ability to analyze text systematically is essential. Students will learn how to collect, process, and analyze text data to answer meaningful research questions.

The course covers a range of methods including dictionary-based approaches, supervised classification, topic modeling, word embeddings, and emerging applications of Large Language Models. Emphasis is placed on practical application through hands-on exercises using the R programming language. By the end of the semester, students will develop an original research project using text as data.

• Instructor: Tamar Mitts
  h-index: 5 Info
• 3 points

Advanced Computing for Policy

Pre-req: DSPC IA6000 - Computing in Context, 
or see option for testing out
. 
 In Computing in Context, students “explored computing concepts and coding in the context of solving policy problems.” Building off that foundation of Python fundamentals and data analysis, Advanced Computing for Policy goes both deeper and broader. The course covers computer science concepts like data structures and algorithms, as well as supporting systems like databases, cloud services, and collaboration tools. Over the semester, students will build a complex end-to-end data system. This course prepares students for more advanced data science coursework at SIPA, and equips them to do sophisticated data ingestion, analysis, and presentation in research/industry.

• Instructor: Aidan Feldman
  Info
• 3 points

Artificial Intelligence Institutions

AI Institutions

Pre-requisites: Microeconomics. Students would benefit from previous coding experience, but software development is not a strict requirement. 
Our institutions were developed in a context with different technologies: where travel and communication were slow and expensive, and thinking had to be done by humans. New technologies afford—and may require—different ways of organizing society. We will consider historical episodes of technological change and our current era, following how shifts in technology can shift the economy and society. We will first use this course itself as a laboratory to explore the impacts of AI on education. We will then consider how AI may reshape other sectors, including governance, transportation, and defense; and the cross-cutting questions it raises about values, economic wellbeing, and purpose.

• Instructor: Daniel C Bjorkegren
  Info
• 3 points

Quantitative Methods in Program Evaluati

Quant Methods-Program Eva

Pre-req: SIPA IA6501 - Quant II. 
The goal of this course is to provide students with a basic knowledge of how to perform some more advanced statistical methods useful in answering policy questions using observational or experimental data. It will also allow them to more critically review research published that claims to answer causal policy questions. The primary focus is on the challenge of answering causal questions that take the form “Did A cause B?” using data that do not conform to a perfectly controlled randomized study. Examples from real policy studies and quantitative program evaluations will be used throughout the course to illustrate key ideas and methods.

First, we will explore how best to design a study to answer causal questions given the logistical and ethical constraints that exist. We will consider both experimental and quasi-experimental (observational studies) research designs, and then discuss several approaches to drawing causal inferences from observational studies including propensity score matching, interrupted time series designs, instrumental variables, difference in differences, fixed effects models, and regression discontinuity designs.

As this course will focus on quantitative methods, a strong understanding of multivariate regression analysis is a prerequisite for the material covered. Students must have taken two semesters of statistics (IA6500 & IA6501 or the equivalent) and have a good working knowledge of STATA.

• Instructor: Alan S Yang
  CULPA: 5 Info
• 3 points

Data Analysis for Policy Research Using

Data for Policy Research

This course develops the skills necessary to prepare, analyze, and present data for policy analysis and program evaluation using R. Building on the foundations from Quant I and II—probability, statistics, regression analysis, and causal inference—this course emphasizes the practical application of microeconometric methods to real-world policy questions. (Note: macroeconomic topics and forecasting methods are not covered.)

The central objective is to train students to be effective analysts and policy researchers. Key questions include: Given the available data, what analysis best informs the policy question? How should we design research, prepare data, and implement statistical methods using R? How can we assess causal effects of policies rather than mere correlations? What ethical considerations arise when working with data on marginalized populations?

Students will learn through hands-on analysis of datasets tied to a range of policy issues, including: caste-based expenditure gaps in India, racial disparities in NYPD fare evasion enforcement, water shutoffs in Detroit, Village Fund grants in Indonesia, public health insurance and child mortality, and Stand Your Ground laws and gun violence. The course culminates in a student-led project on a policy topic of their choosing.

• Instructor: Harold B Stolper
  Info
• 3 points

Economics of Education Policy

Pre-req: SIPA IA6501 - Quant II 
or equivalent quantitative methods course. This course applies empirical economic tools to the study of education policy, with a focus on both K-12 and higher education systems. Topics include class size, peer effects, teacher quality, school accountability, school choice, vouchers, and student incentives. In the context of higher education, the course covers investment in human capital, returns to college, and issues of access and equity across income, gender, and race. Students will engage with contemporary research and develop practical skills through empirical exercises using real data. Emphasis is placed on understanding identification strategies and interpreting results from recent studies.

• Instructor: Cristian Pop-Eleches
  h-index: 23 Info
• 3 points

Conducting Empirical Research in Economi

Empirical Research-Econom

Pre-req: any Quant III course. Instructor Managed Registration. Join Vergil waitlist and apply at 
https://forms.gle/fjM8zkoSx9encCo49
.  
The main outcome of the course will be a complete, novel empirical research paper. During the first half of the course, you will review empirical methods, learn about the structure of a high-quality research paper, and process the data for your project. The focus will be on learning how empirical methods—including not only regression-based causal inference but also data processing and measurement—are used in practice. We will draw on examples of excellent applied economics research papers to highlight best practices. By the middle of the semester, you will be expected to have completed initial analysis of your project. The remaining portion of the semester will be spent revising and improving drafts of the research paper, culminating in a presentation of results and submission of a final, publication-quality research paper.  An emphasis of the class will be on real-world practice of handling, cleaning, and processing data. To this end, students will help build and maintain a database of data sets used for their analyses. Over time, this database will become a resource that future students can draw on for their own analyses.

• Instructor: Jeffrey G Jr Shrader
  Info
• 3 points