Jonathan Kropko headshot
JK

Jonathan Michael Kropko

Assistant Professor
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.

Data Science Capstone 2019 Babylon Microfarms
Source: Babylon Micro-Farms Inc.
September 01, 2019 – August 31, 2020
LASE 2559: New Course in the Liberal Arts
Credits: 1–6
This course provides the opportunity to offer a new topic in Liberal Arts Seminars.
PLAD 4500: Special Topics
Credits: 3
Topics on a variety of Political issues.
PLAD 5993: Independent Study
Credits: 3
Readings and writings from various disciplines relating to Political Science.
DS 6001: Practice and Application of Data 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.
DS 6003: Practice and Application of Data Science II
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.
DS 6011: Data Science Capstone Project Work I
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.
DS 6013: Data Science Capstone Project Work II
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.
DS 6014: Bayesian Machine Learning
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.
PLAD 7100: Political Research with Quantitative Methods
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.
PLAD 7500: Special Topics in Politics
Credits: 1–3
Intensive analysis of selected issues and concepts that are relevant to all subfields of political science.
PLAD 8310: Advanced Quantitative Applications in 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.
PLAD 8320: Advanced Topics in Multivariate Analysis
Credits: 3
A survey and application of multivariate modeling techniques. Prerequisite: PLAD 7090, 7100, or equivalents.
PLAD 8500: Topics in Political Science
Credits: 3
Investigates a selected issue in political science.
SYS 9997: Graduate Teaching Instruction
Credits: 1–12
For doctoral students.