代写PADM-GP 4503.001 | EXEC-GP 4503 Introduction to Data Analytics for Public Policy, Administration,

日期：2025-01-23 06:54

PADM-GP 4503.001 | EXEC-GP 4503

Introduction to Data Analytics for Public Policy,

Administration, and Management

Spring 2025

Course Description

This course aims to establish a first principles understanding of 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.

Course and Learning Objectives

Students will be able to:

●    Explain the value of data, assess data arguments, identify alternatives to using data, and leverage administrative data to ground-truth research data.

●    Structure problems, develop hypotheses, identify assumptions, and reference sources and considerations in a rigorous and transparent manner.

●     Identify, obtain, understand, prepare, and analyze data using standard approaches and industry-standard tools.

●    Package and persuade with data visualization techniques and tools [PowerPoint, Excel, Tableau] to reach specific objectives.

How this Course Relates to Other Courses

This is a foundational course.  There are no prerequisites. It is designed to introduce students to first principles approaches to data analytics to build their comfort in navigating ambiguity, leveraging quantitative skills, and using industry-standard data tools and technologies.

Evaluation

The course will be evaluated through class participation [as measured by short quizzes and exit surveys] (25%), two problem sets (25%), and one final project (50%).  Problem sets will use Excel and PowerPoint, so students should ensure they are familiar with how to access these applications.

Late Policy

Assignments are due on the class dates indicated on the course’s NYU Brightspace site.  Late submission of assignments will lead to a two-point reduction for missing the deadline and another two-point reduction for each day thereafter until submitted.

Course Structure

The class includes lectures, readings, break-out session group work, and independent project work.  Class attendance is critical as the course is structured as an experiential learning course. Students are strongly encouraged to apply approaches and tools learned in the course to their specific sector interests to deepen their content knowledge and understand the forces shaping trends in that sector.