Upon completion of this program, students will be able to:

1. Demonstrate understanding of statistical and computational tools (including R) and advanced techniques for data analysis.
2. Process, understand, and explore a wide class of human-generated information to solve complex problems and produce new knowledge.
3. Apply knowledge of statistical and computational tools to political, social, and institutional problems for data analytic positions in academia, government, and industry.
4. Demonstrate ability to effectively communicate understanding of data analytic techniques and statistical and computational tools.

Political Analysis
Methods of scientific analysis, including research formulation, hypothesis generation and testing, quantitative analysis, and computer techniques.

Statistical Programming in R
The basics of programming using the open source statistical program R. Includes imputing data, performing basic analyses, graphing, data types, control structures and functions in base R, and using packages to expand R's capabilities.

Data Science
This course focuses on the collection, organization, analysis, interpretation, and presentation of data. Topics include the acquisition, cleaning, and imputation of data from a variety of sources; data visualization and graphing; data presentation and packaging; and programming considerations for large datasets. The course uses R packages and programming language.

Advanced Studies in Campaign Management
The Campaign Management Institute (CMI) is a nationally-recognized program designed to train individuals for participation in local, state, and federal political campaigns. Developed and taught by strategists from the Republican and Democratic parties, national campaign consultants, and political scientists, the intensive two-week program serves as a valuable foundation for political activists and campaign managers. The institute comprehensively covers campaign techniques, strategy, and tactics with emphasis on technological developments. Student teams develop a campaign plan and present it to a professional panel.

Statistical Machine Learning
Introduction to statistical concepts, models, and algorithms of machine learning. Explores supervised learning for regression and classification, unsupervised learning for clustering and principal components analysis, and related topics such as discriminant analysis, splines, lasso and other shrinkage methods, bootstrap, regression, and classification trees, and support vector machines, along with their tuning, diagnostics, and performance evaluation.