This course is designed to introduce new students in the economics program (freshmen and external and internal transfers) to the application of economic analysis in academia, business, government and the not-for-profit sector. Students will be exposed to the research and consulting activities undertaken by academic economists and economic practitioners as well as a discussion of the career outcomes of the alumni of the RIT economics program.

Game theory uses a mathematical approach to study situations of strategic interdependence, i.e., situations with two or more players in which each player's decision influences payoffs of other players and players are aware of this fact when making their decisions. Game theory has been applied to understand diverse economic, political and biological phenomena. We will study how to formulate situations of strategic interdependence as game theoretic models; how to explain/predict behavior of the parties involved, through the use of various equilibrium concepts; and/or identify guidelines for appropriate behavior. The concepts and methods will be illustrated with many examples. The objective is to introduce you to language of game theory and its methodology, and to develop analytical reasoning skills.

The course will teach students how to perform data wrangling and exploratory data analysis in social science contexts using a general-purpose programming language such as Python or Julia. No prior programming experience is assumed. Students will first learn the basics of the programming language used. They will learn about important sources of social science data and how to retrieve data both manually and programmatically using API access to some of these sources. They will learn how to work with data in different file formats (such as CSV, JSON, DTA, RDA XLSX etc.) and about different types of data (numeric, categorical, ordinal, dates and times etc.), and how to work with and express these different types correctly in the chosen programming-language. Students will study how to combine data from multiple sources, identify and deal with missing observations, how to identify outliers, and how to get data in a form that is more amenable for analysis. Students will use various publicly available data sets, such as data on housing prices, macroeconomic indicators and crime. They will identify important characteristics of data by calculating various statistical measures and constructing different types of charts, plots and visualizations. Students will also learn how to work with statistical distributions such as Binomial, Normal, Poisson and Exponential distributions in the chosen programming language to simulate stochastic phenomena in economics and social sciences.

This course registers the student’s completion of the capstone experience required for economics majors. The requirement can be fulfilled by either presenting a class paper at an approved on-campus or off-campus research conference or submitting a solo-authored or co-authored research paper to a peer-reviewed journal. Economics students experience conducting research and presenting their findings before an audience of their peers and professionals in the field. Students are sponsored by a faculty member, developing their pre-professional skills while learning how to do research first hand. Double-majors who satisfactorily complete a capstone experience in their primary major automatically fulfill the economics capstone experience requirement.