15.053 Optimization Methods in Business Analytics
Introduces optimization methods with a focus on modeling, solution techniques, and analysis. Covers linear programming, network optimization, integer programming, and nonlinear programming. Applications to logistics, manufacturing, data analysis, transportation, marketing, project management, and finance. Includes a project in which student teams select and solve an optimization problem (possibly a large-scale problem) of practical interest.
Business Analytics and 15.053
As of Fall 2016, the Sloan School will be offering a major (as well as a minor) in Business Analytics. With the growing availability of electronic data, Business Analytics provides a sophisticated toolkit of approaches for transforming that data into valuable information, models, and decisions. Demand for this expertise is high and rising: McKinsey Global Institute estimates a shortfall of between 140,000 and 190,000 professionals in Business Analytics by 2018, a 50-60% gap relative to projected supply.
15.053 is a core requirement of the major because optimization methodology is a core discipline of Business Analytics. For example, firms approach business decisions with a goal of minimizing costs or maximizing net revenue. In addition, statistics and machine learning (two other disciplines of Business Analytics) both rely heavily on optimization models.
During the first half of 15.053, we focus on modeling methodologies for optimization. In the second half, we focus on the theory of optimization, including the algorithms underlying linear and integer optimization. Throughout, we will present material in rigorous manner that is well suited for majors or minors in Business Analytics.
What is the speaker series?
On Wednesdays at 4pm, we will have a series of guest speakers discussing issues of optimization in practice. Speakers will present for 30 to 40 minutes, leaving 10 to 20 minutes for questions. There will be 10 talks during the semester, including the following:
Students in 15.053 will be assigned to teams of four in order to carry out a project exploring optimization in practice. The instructor and TAs will provide a set of project topics that we believe will work well. In addition, students can suggest their own topics.
Below are some project topics that have been used in the last two years and that may be suitable as topics again this year. (More potential topics will follow.)
Changes to 15.053.
This year, to make space for the guest lecture series, we have reduced the number of weekly lectures from two to one. We have done this by moving much of the lecture material to videos supported under the edX system. This system alternates videos with questions, providing students with immediate feedback so that they can gauge their mastery of the material and move more quickly to advanced concepts.
We are also changing the way in which student are assigned to project teams. In our new approach, we will ask students to list their preferred topics. Then using an optimization model, we will assign students to teams and topics. We believe that this approach will ensure that each student works on a topic of interest while also leading to teams that share a common goal. The TAs and instructor will provide support throughout the semester to student teams so that they can stay on track.