Aidan Feldman

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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.

With an influx of data and an increased preference for using algorithms to drive decisions, this course builds on how public policy professionals should discern the correct data sources to use and how to interpret the accompanying algorithm-driven results. Since data and algorithms can lead to false positive and false negative results that adversely shape the impact of public policy decisions, this course exposes students to common data biases that influence how public policy professionals understand, use, and interpret the world.

At the end of the course, students will write basic code using the Python programming language and have a firm foundation for data analysis. To gain a practical context beyond the readings, students are encouraged to attend events and follow studies put together by NYU’s The AI Now Institute, which produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on public issues.

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. Students will learn how to scope a policy problem, understand the data generation process, how to manage, combine, and structure data, and how to create, measure and analyze the effect of different data decisions. They will also learn the basics of machine learning and visualization as well as inference, bias, privacy, and ethics issues. It is designed for graduate students who are seeking a stronger foundation in data analytics, although undergraduates with strong foundations in data and statistics will be considered for admission. The professor is a former senior White House advisor on the Federal Data Strategy and member of two White House Committees: the Advisory Committee on Data for Evidence Building and the National AI Research Resources Task Force.   

The online textbook provides more information.