Ethics, Public Policy and Emerging Technology

Commentators have noted that the use of AI technologies in government and nonprofits is lagging and part of the reason for this could be that many public service leaders do not feel equipped to make decisions on the use of these technologies. This course aims to equip current and future leaders with an understanding of how emerging (or AI type) technologies are created and how they work and to provide a foundation for thinking strategically and ethically about their use in a variety of settings. This is a non-technical class: no coding knowledge is required.

Topics in Public Policy: Data, Evidence, Ethics, and Bias in an AI World

The importance of data and evidence in making decisions is critical to success in both the private and the public sector. The role of Artificial Intelligence (AI) in making sense of the vast amounts of data available to make evidence-based decisions has been highlighted by the establishment of a National Artificial Intelligence Initiative and by its pervasive use in virtually all parts of the economy and society. Yet there is potentially a dark side to the implementation of AI, particularly given its widespread use in criminal justice and immigration.

Data and AI Strategies for Social Impact Organizations

Data plays an increasingly important role in powering today’s enterprises, governments and society as a whole. With the rapid pace of innovation, data science, advanced analytics and Artificial Intelligence (AI) are becoming increasingly central and critical to business today. Over time, social impact organizations will deem these tools as core to achieving their mission.

Python Coding for Public Policy

This 7-week course exposes the students to the application and use of data analytics in setting public policy. The course does so by teaching introductory technical programming skills that allow students to learn and apply Python code on pertinent public policy data, while emphasizing on applicability. The course is accompanied by readings for each class in order to contextualize why data analytics supplements but doesn’t replace the student / professional role in setting public policy.

R Coding for Public Policy

R is a powerful open source language and environment for statistical computing and graphics. R provides a wide selection of statistical and graphical techniques. It is rapidly becoming the leading language in data science and statistics. R can easily tackle linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering and more.

Data Visualization and Storytelling

In our increasingly data-reliant and data-saturated society, people who understand how to leverage data to generate insights have the power to change the world. Data visualization and storytelling is a crucial skill for policy and data analysts, communications and marketing professionals, and managers and decision-makers within nonprofits, social organizations and the government. With the advent of visualization tools that do not require coding, data storytelling in the digital age is also an attainable skill set for people with varying levels of technical ability.

Advanced Data Analytics and Evidence Building

The goal of this course is to develop the key data analytics skill sets necessary to inform evidence-based policy. Its design offers hands-on training in how to make sense of and use large scale real world heterogeneous datasets in the context of addressing real world problems. The main learning objectives are to develop a better understanding of how to develop and apply new techniques to analyze social issues using data from a variety of different sources. It is designed for graduate students who are seeking a stronger foundation in data analytics.

Designing Data Collection for Program Evaluation, Policy, and Management

This half-semester course will include a qualitative and a quantitative component. We will begin with a discussion of using focus groups as a tool for data collection. We will spend the rest of the course talking about survey designs (including probability and non-probability sampling) and questionnaire development, as well as implementation issues. Students will produce a short focus-group proposal or survey questionnaire.

The course video provides more information.

Introduction to Data Analytics for Public Policy, Administration, and Management

The goal of this course is to establish a first-principles understanding of the qualitative and quantitative techniques, tools, and processes used to wield data for effective decision-making. Its approach focuses on pragmatic, interactive learning using logical methods, basic tools, and publicly available data to practice extracting insights and building recommendations. It is designed for students with little prior statistical or mathematical training and no prior pre-exposure to statistical software.

Using Large Data Sets in Policy Research

This half-semester course will focus on the analysis of complex survey data. The focus will be on using secondary data of this type. Using Stata, we will discuss how regression analysis differs when using complex survey data as well as data-cleaning procedures, including trimming, variable transformations, and dealing with missing data. Students will produce a short memo, using real survey data, to analyze a simple research question.

Introduction to Database Design, Management, and Security

The goal of this course is to train advanced students on the principles, practices, and technologies required for good database design, management, and security. An introduction to the concepts and issues relating to data warehousing, governance, administration, security, privacy and alternative database structures will be provided. The course concentrates on building a firm foundation in information organization, storage, management, and security.

Large Scale Data Analysis I

The past decade has seen the increasing availability of very large scale data sets, arising from the rapid growth of transformative technologies such as the Internet and cellular telephones, along with the development of new and powerful computational methods to analyze such datasets. Such methods, developed in the closely related fields of machine learning, data mining, and artificial intelligence, provide a powerful set of tools for intelligent problem-solving and data-driven policy analysis.