Computing in Context

This introductory course will explore computing concepts and coding in the context of solving policy problems. Such problems might include troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy or determining the impact that local financial markets have on international healthcare or education. Using policy scenarios as examples, students will be exposed to topics including: requirements gathering, data collection, data cleansing, writing pseudocode and code, using Python packages to help solve policy problems, presenting technical solutions and the constraints of computing. The hands-on nature of the class will help students to develop a strong, transferable skill-set that could be applied to both current coursework and future employment. Between the computer science and policy context lectures, students will see how computer science will become a fundamental component of their policy analysis education.

• Instructor: Aidan Feldman
  Info
• 3 points

Computing in Context

This introductory course will explore computing concepts and coding in the context of solving policy problems. Such problems might include troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy or determining the impact that local financial markets have on international healthcare or education. Using policy scenarios as examples, students will be exposed to topics including: requirements gathering, data collection, data cleansing, writing pseudocode and code, using Python packages to help solve policy problems, presenting technical solutions and the constraints of computing. The hands-on nature of the class will help students to develop a strong, transferable skill-set that could be applied to both current coursework and future employment. Between the computer science and policy context lectures, students will see how computer science will become a fundamental component of their policy analysis education.

• Instructor: Aidan Feldman
  Info
• 3 points

Computing in Context

This introductory course will explore computing concepts and coding in the context of solving policy problems. Such problems might include troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy or determining the impact that local financial markets have on international healthcare or education. Using policy scenarios as examples, students will be exposed to topics including: requirements gathering, data collection, data cleansing, writing pseudocode and code, using Python packages to help solve policy problems, presenting technical solutions and the constraints of computing. The hands-on nature of the class will help students to develop a strong, transferable skill-set that could be applied to both current coursework and future employment. Between the computer science and policy context lectures, students will see how computer science will become a fundamental component of their policy analysis education.

• 0 points

Computing in Context

This introductory course will explore computing concepts and coding in the context of solving policy problems. Such problems might include troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy or determining the impact that local financial markets have on international healthcare or education. Using policy scenarios as examples, students will be exposed to topics including: requirements gathering, data collection, data cleansing, writing pseudocode and code, using Python packages to help solve policy problems, presenting technical solutions and the constraints of computing. The hands-on nature of the class will help students to develop a strong, transferable skill-set that could be applied to both current coursework and future employment. Between the computer science and policy context lectures, students will see how computer science will become a fundamental component of their policy analysis education.

• 0 points

Gender Data for Gender Equality

MIA and MPA Policy Skills II Core. 
In recent years, despite enhanced awareness about the magnitude and multifaceted nature of gender inequalities on the one hand, and the promises of the ‘Data Revolution’ including AI on the other hand, gaps remain in both data availability and usage of 'Gender Data' that aim to both capture the underlying dynamics, drivers and outcomes of gender inequalities, and promote gender equality. The #MeToo movement and the COVID-19 pandemic in particular highlighted both the salience and implications of gender inequalities, including the “shadow pandemic” of sexual and gender-based violence, and, indeed, the dearth of quality data on these issues. In this context, the goal of this course is to train advanced students on the historical and latest discussions, opportunities, challenges, requirements and limitations of leveraging various types of data to fill ‘gender data gaps’ and promote gender equality, and equip them with practical ressources and tools to shape current and future debates and policies. It is designed as an intermediate-level course on the issue that touches on its historical, sociopolitical, cultural and economic dimensions and technical and analytical aspects related to data access, reliability, and the political economy and ethics of collecting, analyzing and using data for social change. It fundamentally seeks to ask and partially address the question of whether and how data, including ‘traditional’ data (such as official statistic and quantitively and qualitative survey data) and non-traditional data (such as social media and online data, telecom operators’ data, satellite imagery) can be leveraged concretely to pursue greater gender equality through analysis, advocacy and policy. It will also discuss risks associated with data collection and analysis and digital technologies more broadly, including those related to privacy and safety, biases, harassment, discrimination, and challenges and requirements for making these data matter, i.e., have a causal impact on what is measured. In doing so, it will zoom in on a few sensitive themes, including sexual and gender-based violence (SGBV), child marriage and female genital mutilation (FGM), sexual orientation and Gender Identity (SOGI), social norms, as well as socioeconomic and political empowerment and inclusion, especially although not exclusively in countries and regions of the “Global South” (or “Global Majority&rd

• Instructor: Emmanuel F Letouze
  Info
• 1.5 points

Python for Public Policy

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. 
Alternate title: "How to Use a Bit of Code to Do Things That Would Be Really Hard in Spreadsheets." Students will learn data analysis through the Python programming language—exploring, manipulating, visualizing, and interpreting open data to answer policy questions. The class incorporates use of generative AI for coding problems, helping students understand its strengths and weaknesses. No coding experience required.

• Instructor: Rebecca S Krisel
  Info
• 1.5 points

Data Analytics for Public Policy, Admin

Intro to Data Analytics

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. 
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. Priority Registration: MIA and MPA. 
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

Intro to Text Analysis in Python

Intro-Text Analysis-Pytho

MIA and MPA Policy Skills II Core. 
This course introduces students to foundational concepts and methods for analyzing text-as-data using Python. Designed for beginners with no prior coding experience, the course emphasizes hands-on learning and practical applications across disciplines. Students will explore computational techniques for collecting, cleaning, and analyzing text data from sources such as news media, social media, and websites. Topics include web scraping, working with APIs, sentiment analysis, topic modeling, named entity recognition, and more. The course will also examine the role of generative AI in building custom scripts for data collection and analysis.

Through guided instruction and project-based learning, students will develop beginner-to-intermediate Python programming skills, understand core principles of data analysis, and gain experience using Python to explore research questions relevant to policy, media, business, and technology. The course culminates in a final project that may serve as a portfolio piece for job seekers or public scholarship.

• Instructor: Rebecca S Krisel
  Info
• 1.5 points

Introduction to Infographics and Data Vi

Intro to Data Visualizati

MIA and MPA Policy Skills II Core. Priority Registration: MIA and MPA. 
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

Generative AI

MIA and MPA Policy Skills II Core. 
This course introduces students to the fundamentals of Generative Artificial Intelligence (Generative AI), with a focus on how these technologies are built and their implications for society and public policy. Students will gain an understanding of language models, large language models (LLMs), deep learning, transformers, and Generative Pre-Trained Transformers (GPT).

In addition to technical foundations, the course explores the societal and policy dimensions of Generative AI, including algorithmic bias, ethical challenges, labor market disruption, and regulatory frameworks. Designed for students with varied technical backgrounds, the course equips future policy professionals with the tools to engage critically with emerging AI technologies.

• Instructor: Sameer R Maskey
  Info
• 1.5 points

Applied Econometrics

This course equips students with the tools to critically evaluate empirical research through the lens of causal inference. Emphasizing real-world policy relevance over statistical correlation, it introduces students to identification strategies that approximate randomized trials using observational data. Students will explore advanced econometric methods, including instrumental variables, difference-in-differences, fixed effects, regression discontinuity, and synthetic controls, while examining their strengths and limitations in drawing causal conclusions.

Designed for students with prior coursework in quantitative methods (U6500 and U6501), this course stresses conceptual rigor and applied skills. Assignments include STATA-based replication exercises, a research design proposal, and seminar engagement. Readings and examples draw from policy-relevant domains such as health, education, and environmental economics. Students will leave the course with a deeper understanding of how to produce, assess, and apply causal evidence to inform public decision-making.

• Instructor: Douglas V Almond
  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

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

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.

• 0 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.

• 0 points