3.00 credit hours
Over the past two decades, computational methods have become an indispensable tool in social science studies. The goal of this course is to introduce undergraduate students to numerical methods and computer implementations for conducting modern quantitative research in economics and social sciences. In this course, we will learn about how to utilize computational methods to conduct research in several different domains, including microeconomics, macroeconomics, financial market, and empirical methods. At the conclusion of this course, students will be able to effectively apply quantitative solution methods to a wide range of economic, financial, and business issues. In addition, students will learn Python as a basic programming language. The learned programming skills will be readily applicable out of classroom. Computational economics will provide students a comprehensive experience and training in economics, computer science, and statistics. Students will be able to distinguish themselves on the job-market, as candidates ready to work in an environment that requires both economics insights and strong quantitative data/computational skills. The course will also be highly useful for students who plan to go to graduate school in either economics, business, finance or statistics. Recommended preparation but not required: basic programming experience (e.g. using Python, R, Matlab, Stata).
No Syllabus Available