Statistical Analysis

In this course students will develop expertise in using the tools necessary to collect, analyze, and visualize large amounts of text. The course begins with a hands-on introduction to the programming concepts necessary to collect and process textual data. The course then proceeds to cover key statistical concepts in machine learning and statistics that are used to analyze text as data. Throughout the course, students will develop a research project that culminates in the display of results from a large-scale textual analysis. Prerequisite: 470.681 Probability and Statistics.

With the passage of the Foundations for Evidence-Based Policymaking Act, all federal agencies are now required to make data accessible to the public and to implement specific plans for developing statistical evidence to inform policymaking. This course will examine the ways in which evidence and expertise are now being used for policy development and assessment. Specific topics will include cost-benefit analysis, cost effectiveness analysis, contingent valuation, forecasting and the communication of statistical evidence. In addition, the course will explore the interplay between political decisionmakers, experts and citizens in the evidence-based policymaking process.

This course reviews the mathematical principles that are fundamental to quantitative analysis. The course covers functions, probability theory, integral and derivative calculus and matrix algebra.

This class applies data analytic skills to the urban context, analyzing urban problems and datasets. Students will develop the statistical skills to complete data-driven analytical projects using data from city agencies, federal census data, and other sources, including NGOs that work with cities. We will examine a variety of data sets and research projects both historical and contemporary that examine urban problems from a quantitative perspective. Over the course of the term, each student will work on a real-world urban data problem, developing the project from start to finish, including identifying the issue, developing the research project, gathering data, analyzing the data, and producing a finished research paper. Prerequisite: 470.681 Probability and Statistics

Learning the basics of Python empowers analysts to retrieve and leverage data in new ways. After covering the fundamentals of syntax, students learn how to read, create and edit data files using Python. Building on that knowledge, students interact with online resources through bulk data APIs and web scraping. Finally, students will use the data they collect to develop an original analysis. Prerequisite: 470.681 Probability and Statistics

This course explores technological and data-driven solutions for policy challenges. This includes developments within government, such as the new types of leadership provided by Chief Innovation or Chief Data Officers, the trend toward digitalization of services, and the movement toward open data. It also covers innovation by citizens through the civictechnology movement. Civic tech initiatives have been used to extend and improve services, increase efficiency, design applications for citizen engagement, and improve communication across a variety of policy domains. The course also covers the concept of smart cities and how it can be understood as both new applications of technology (such as sensors and smart infrastructure), and the strategic use of data. For the course project, students will evaluate a policy initiative using city open data, policy research, an analysis of political culture within which the initiative would be implemented, and the technology that could be used for the initiative. Some familiarity with R programming language and theRStudio environment is helpful. Prerequisite (one of the following): 470.768 Programming and Data Management; or experience with statistical programming and instructor permission.

Analytics inform the decision-making process, strategizing, and forecasting of modern American campaigns. This course focuses on the role that analytics play in campaigns and elections in America. Campaign strategists, policy analysts, and social scientists leverage data from voter rolls, consumption and public opinion polls to make better choices. This course surveys the theoretical and empirical literature in American electoral politics to examine how campaigns and political organizations are using field experiments, microtargeting, and public opinion polling to tackle the challenges of getting out the vote and increasing registration and voting rates. Other topics covered include voting behavior, public opinion, partisanship, and campaign finance. Students will gain a rich understanding of how analytics has become a key component of the electoral process. Students will also gain experience analyzing data through simulations and data analysis exercises. Prerequisites: 470.681 Probability and Statistics

This course provides students with a strong foundation in database architecture and database management systems. Students will evaluate the principles and methodologies of database design and techniques for database application development. Students will also examine the current trends in modern database technologies such as Relational Database Management Systems (RDBMS), NoSQL Databases Cloud Databases, and Graph Databases. Prerequisite: none

This course is a comprehensive examination of all aspects of designing questionnaires, conducting survey research, and analyzing survey data. The class will cover question construction, measurement, sampling, weighting, response quality, scale and index construction, IRBs, ethics, integrity and quality control, modes of data collection (including telephone, mail, face to face and focus groups), post collection processing and quantitative analysis of data (including chi-square and ANOVA), as well as report writing fundamentals. The class culminates by fielding a survey of student created questions and writing an executive summary of the survey with a paper discussing the research findings. Prerequisite: 470.681 Probability and Statistics

Data science is a methodology for extracting insights from data. This course is an introduction to the concepts and tools that are used in data science with an emphasis on their application to public policy questions. The course covers some advanced data mining and machine learning processes including classification and decision trees, random forests, cluster analysis, and outlier detection, while also providing you with training in the basics of data management and data exploration. All of the work in the course will be conducted to prepare you to proficiently conduct predictive analytics in a real-world setting. Some familiarity with R programming language and the RStudio environment is necessary. Prerequisite: 470.681 Probability and Statistics

This course provides insights into how to utilize shared cloud computing resources through a service provider. These resources can be storage space, software as a service, or compute servers. This is a hands-on course in which students will access a variety of cloud services and work with different cloud providers such as Apple, Microsoft, Google, and Amazon. Students will set up virtual servers, work with cloud file storage, learn about a variety of cloud collaboration options, and much more. This practical course will help students make the transition to working in the cloud from any device, anywhere, anytime. All areas of the public sector, such as education, healthcare and law enforcement, increasingly use cloud computing both to deliver information to clients and share information within and across agencies. No prerequisite