The course is intended to provide a broad-based introduction to numerical and data-science methods commonly used in economics. The course will first introduce students to the R programming language, assuming no prior experience. Subsequent lectures, using R, will provide students an opportunity to apply this knowledge to real-world data to achieve an economic objective. The methods used in these applications will include (but are not limited to): collecting, cleaning, merging, processing, and visualizing data, descriptive analysis, optimization, and supervised/unsupervised statistical learning. In addition, the course has an experiential component that connects students with industry leaders in economic applications of data science through a series of on-campus events.

How does a risk averse individual allocate their funds? Students begin by defining and measuring risk, making connection to their microeconomics training. They then develop and use asset pricing models to explore the interplay between risk and return. Finally, students use these tools to develop a mean-variance optimal portfolio allocation. Students are introduced to basic quantitative tools and participate in myriad practical applications.

This course applies microeconomic techniques to study the allocation of resources in professional and collegiate sports, the determinants of an athlete's salary, racial discrimination, gender wage differentials, and the economic impact of sports within the local community. The course engages students with real-world sports stories and incorporates empirical research and statistical analysis to test different economic theories.

This course uses basic microeconomic theory and recent empirical studies to examine the causes and consequences of gender differences in economic outcomes. Topics covered may include family formation and dissolution, fertility decisions, human capital investment, labor force participation, the gender earnings gap, and occupational choice.

Building upon the foundation developed in ECON 425, students take part in each of the five steps of the asset allocation process (explore, explain, predict, allocate, and protect) by addressing the following questions. How does the modern financial economist acquire, clean, and transform data? What drives asset returns? Can we forecast returns? How do we form a portfolio in the presence of risk? How do we assess and manage risk?

This course covers empirical and numerical techniques for the study of market power and market failure. The covered methods are commonly applied in antitrust and regulatory policy analysis, economic consulting, and quantitative marketing studies.

Get your hands dirty with health data. Learn the econometric tools necessary for empirical analysis of health-related outcomes and behaviors. Use economic theory to construct hypotheses and use econometrics to measure effects of individual, organizational, and public policy determinants. Empirically analyze detailed data to understand medical care demand, health behaviors, and health production. Interpret statistical findings using health-related applications.

This course will apply and build on existing economic theory and econometric skills to study education policy. Topics include education production, teacher quality, and investment in education, with a particular emphasis on recent policies aimed at reducing inequality. Students will learn how to conduct and how to critique empirical studies in economics of education.

Analysis and interpretation of selected problems and policy issues. Content varies, but attention is given to such topics as trade barriers, trade patterns, floating exchange rates, and international monetary policy.

The course is designed to advance analytical skills in the field of development economics. The course covers empirical models of development gap, economic growth, human capital, income inequality, corruption, immigration, informality, and current COVID-19 crisis. Students will learn how researchers evaluate development policies, including tax and welfare reforms, education programs, privatization, regulation, immigration, and COVID-19-related policies. Students will improve programming skills in Stata and use real-world survey data in writing a research project.

A theoretical and empirical analysis of current social problems involving individuals and their jobs. Included are such topics as poverty, discrimination, and working conditions.