JK
Department: Data Science School
Office location and address
S383 Gibson Hall
31 Bonnycastle Dr
Charlottesville,
Virginia
22903
Education
Ph.D. in Political Science,University of North Carolina at Chapel Hill
Biography
Jonathan Kropko attended Ohio State University, and then received his Ph.D. in political science from the University of North Carolina in 2011. He recently completed a postdoctoral research fellowship in applied statistics at Columbia University. His research involves the development of new statistical techniques to facilitate research in political and the social sciences. He is currently working on methods to examine historical data, to test theories of voting in U.S. presidential elections, and to handle nonresponse in surveys. Jonathan will be teaching graduate seminars in quantitative research methodology.
Publications
Sponsored Awards
Data Science Capstone 2019 Babylon Microfarms
Source: Babylon Micro-Farms Inc.
September 01, 2019 – August 31, 2020
Courses
Credits: 1–6
This course provides the opportunity to offer a new topic in Liberal Arts Seminars.
Credits: 3
Topics on a variety of Political issues.
Credits: 3
Readings and writings from various disciplines relating to Political Science.
Credits: 2
This course covers the practice of data science practice, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.
Credits: 1–2
This course covers the practice of data science practice, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Students will use their capstone projects to explore the impact of data science on that domain.
Credits: 1
This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.
Credits: 1–2
This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.
Credits: 3
Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference.
Credits: 4
Introduces probability and statistics as tools for quantitative political science analysis. Covers basic probability theory, descriptive statistics, and statistical inference with focus on the specification and interpretation of the regression model. Weekly homework assignments allow students to practice applying the concepts and methods from class. The course requires no prior experience with statistics.
Credits: 1–3
Intensive analysis of selected issues and concepts that are relevant to all subfields of political science.
Credits: 3
Considers the use of selected techniques of behavioral research in the study of government and foreign affairs. Emphasizes the assumptions, procedures, and applications of the techniques rather than substantive findings. Prerequisite: PLAD 7090, 7100, or equivalents.
Credits: 3
A survey and application of multivariate modeling techniques. Prerequisite: PLAD 7090, 7100, or equivalents.
Credits: 3
Investigates a selected issue in political science.
Credits: 1–12
For doctoral students.