Optimization Methods in Business Analytics

This course will examine optimization through a business analytics lens. Students will learn the theoretical aspects of linear programming, basic Julia programming, and proficiency with linear and nonlinear solvers. Theoretical components of the course are made approachable and require no formal background in linear algebra or calculus. The primary focus of the course is optimization modeling. As a six-week subject, it covers about half of the material of the MIT OpenCourseWare version, {{% resource_link "24bfc6a4-9014-45aa-838b-2eed2c51a9d9" "15.053 Spring 2013" %}}. The topics of the 2013 subject were optimization modeling, algorithms, and theory. As part of the {{% resource_link "ccaebafc-0213-4bc8-89f7-f63b3e76013f" "Open Learning Library (OLL)" %}}, this course is free to use. You have the option to sign up and enroll if you want to track your progress, or you can view and use all the materials without enrolling. Resources on OLL allow learners to learn at their own pace while receiving immediate feedback through interactive content and exercises.

This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice.