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.

Students will learn the fundamentals of web development with HTML, CSS, and Javascript, and github.   Using Free and Open Source Spatial Data tools, students will learn to bring their maps to life on the web as interactive experiences.  Use tools like QGIS, CartoDB and PostGIS.  Final project will be an interactive web map around an Urban topic of your choosing.

Multiple regression is the core statistical technique used by policy and finance analysts in their work. In this course, you will learn how to use and interpret this critical statistical technique. Specifically you will learn how to evaluate whether regression coefficients are biased, whether standard errors (and thus t statistics) are valid, and whether regressions used in policy and finance studies support causal arguments.

The goal of this course is to provide students with an introduction to advanced empirical methods. We begin by discussing a framework for causal inference and how randomized controlled trials provide a simple and powerful template for thinking about causal questions. We then develop a sequence of advanced empirical methods as alternatives to randomized trials, in settings where experiments are infeasible or not desirable. In particular we discuss regression discontinuity, matching methods, difference-in-differences and panel data, and instrumental variables.