Decision Models & Analytics

One of the most crucial skills for a modern manager is knowing how to use data to make decisions. In Decision Models & Analytics, you will learn how to use modern analytics tools, such as optimization and simulation, to solve complex business problems. Whether you want to pursue a career in finance, consulting, technology, operations or marketing, knowing how to model and solve complex problems will make you a more effective decision-maker and give you a competitive edge.

The course is hands-on. In class, you will learn how to model and solve the following problems, among many others:

Online Advertising

If you own a website, how do you maximize the ad revenues you earn from it?

linear optimization

Online Dating

How do you match couples to maximize compatibility?

integer optimization

Portfolio Optimization

How do you build a portfolio of stocks that maximizes return and minimizes risk?

nonlinear optimization

Online Retailing

How do you get your products delivered to your customers in the fastest and cheapest way possible?

network flows

New Product Development

Given design risks and market uncertainty, how do you decide which products to develop?

Monte Carlo simulation

Course Instructors:

Professor Arash Asadpour
Professor David Juran
Professor Ilan Lobel
Professor Lucius Riccio
Professor Jiawei Zhang

Course Offerings:

In the winter and spring of 2018, we will offer the following sections. For the syllabus, please select a particular section.

Graduate Courses (OPMG-GB.2350)


- Winter 2018, Prof. Zhang
  Variable Hours (see syllabus)


- Spring 2018, Prof. Lobel (syllabus)
  Mondays and Wednesdays 9:00-10:20am


- Spring 2018, Prof. Lobel (syllabus)
  Mondays and Wednesdays 10:30-11:50am


- Spring 2018, Prof. Zhang
  Thursdays 6:00-9:00pm

Undergraduate Courses (MULT-UB.0007)


- Spring 2018, Prof. Juran
  Mondays and Wednesdays 3:30-4:45pm


- Spring 2018, Prof. Riccio
  Tuesdays and Thursdays 2:00-3:15pm

Pre-requisites:


Although there are no specific pre-requisites for this class, prior knowledge of basic probability concepts (probability distributions, percentiles, expected value, standard deviation, variance and covariance) would be helpful.