The course introduces basic numerical methods often used in econometrics, quantitative social science, and data science. Topics include random sampling, numerical integration, numerical differentiation, optimization, simulation, and maximum likelihood estimations. The theories are introduced at an accessible level, and the focus is on the application of the methods.

Equally important in this course is introducing students to computer programming using Julia. Students are asked to code functions to implement the numerical methods, and hands-on exercises are given to hone coding skills. Comprehension of the numerical methods and development of programming skills are mutually reinforcing and complementary to one another.

This two-credit course is not intended to provide comprehensive coverage of advanced numerical or programming methods. Rather, it aims to lay a solid foundation for students to tackle computational challenges with confidence by developing their coding and problem-solving skills.

The course will be structured around two weeks of online video lessons, followed by a week of in-class discussions.