MS in Data Analytics and Policy Degree Details and Courses

This course instructs students in various visualization techniques and software, including R, Tableau, and vector graphics software (e.g., Adobe Illustrator, Inkscape). Students will learn how to ask interesting questions about politics; identify data that can be used to answer those questions; collect, clean and document the data; explore and analyze the data with statistical and graphical techniques; and create compelling, informative and accurate visualizations and present these visualizations to educated audiences. Prerequisite: 470.681 Probability and Statistics

This course introduces students to the fundamentals of statistical analysis as well as the R programming language and RStudio environment. Students will learn the building blocks of descriptive and causal inference, including summary statistics, survey sampling, measurement, hypothesis testing, linear regression and probability theory. Students will also learn how to create data visualizations in R, including times series plots, scatter plots and bar graphs. In addition, students will focus on interpreting statistical findings and presenting results in a compelling manner. By the end of the course, students will be able to conduct a statistical analysis to answer a meaningful policy question and will be prepared to take more advanced methods courses. Prerequisites: none

Regression modeling is the workhorse of data science and statistical analysis. This course builds on the foundation from Probability and Statistics and provides a more rigorous survey of this family of techniques. Specific topics include measures of fit, logistic and probit regression, interaction terms, model specification, and other important aspects of conducting regression analysis. The focus of the course is on using statistics in an applied manner to address meaningful research questions. Prerequisites: 470.681 Probability and Statistics

This course introduces students to statistical programming. Computer programming languages are important tools for performing data analytics, statistics, machine learning, data visualization, and much more. By the end of this course, students will understand fundamental programming concepts that apply to all programming languages. These concepts include variables, functions, loops, data structures, and data types. The course will also cover the use of these tools to solve challenging data problems that students may encounter in their academic or professional careers. Note: The course overlaps a small amount with 470.681 Probability and Statistics, but this course focuses much more heavily on the fundamentals of programming. No prerequisite.

This course is for students who are completing their M.S. in Data Analytics and Policy (formerly M.S. in Government Analytics). The course guides through the process of developing and executing an original data analysis project aimed at addressing an issue related to public policy, politics or governance. Students will formulate an empirical research question and answer that question using a quantitative analysis that makes an original, scholarly contribution. To complete the project, students will use the skills, tools and knowledge they have acquired throughout the program. Students should take this course in their final term (or penultimate term with permission from their advisor).

Prerequisites: All other core courses

For M.S. in Data Analytics Students: 470.681 Probability and Statistics, 470.768 Programming and Data Management, 470.709 Quantitative Methods, 470.673 Data Visualization

For M.S. in Government Analytics Students: 470.681 Probability and Statistics, 470.709 Quantitative Methods, 470.710 Advanced Quantitative Methods, advanced methods course from approved list (see program website)