| ESOC 214: Introduction to Data Science |
 As data continue to penetrate everything we do in contemporary work across many professions, employers are seeking data savvy people to extract meaning and patterns from data. This course provides an introduction to the various skills and considerations required for data management and analysis in business, education, and science. Particular attention is given to learning how to use the free and open-source computing environment R, as well as its data visualization package ggplot2. Highlights:
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| ESOC 302: Quantitative Methods for the Digital Marketplace |  This course explores research paradigms and theoretical approaches that inform contemporary social science research, including various study designs and methods of data analysis. Though this course introduces research methods from across the academic spectrum, quantitative analysis of both small and large data sets is emphasized. Students learn about basic statistical analysis and will be introduced to the emerging worlds of computational social science and social network analysis. Highlights:
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| ESOC 414 / INFO 514: Computational Social Science |  This course guides students through advanced applications of computational methods for social science research. Students are encouraged to consider social problems from across sectors, including health science, environmental policy, education, and business. Particular attention is given to the collection and analysis of data to study social networks, online communities, electronic commerce, and digital marketing. Students consider the many research designs used in contemporary social research, including “Big” data, online surveys, and virtual experimental labs, and think critically about claims of causality, mechanisms, and generalization. Highlights:
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