Students immerse themselves in courses on data analysis and competitive strategy with Professor Ellickson, economics and marketing faculty.

Leaders in the classroom and beyond.

Renowned for their excellence in research, instruction, and innovation, our faculty will help you master your chosen discipline and prepare you for a career in research and academia. A PhD from Simon Business School gives you the skills and credentials to lead in the classroom, and to undertake innovative, industry-shaping research. 




Choose from


Depending on your skills and goals.

Getting Started

Renowned for our analytical rigor and quantitative focus, the academic component of Simon’s PhD program is rooted in economics and statistics. That’s why our program begins with our Summer Math Camp. Your coursework will deepen your understanding of state-of-the-art concepts and tools, while your faculty collaborations explore the complex process of modern business and economic research to fuel your own independent research. Deeper specialization in coursework occurs in the second year when you’ll concentrate on your major field of study.

Learn from mentors and masters.

Our faculty are world-renowned for their excellence in academic research. They are also inspiring, effective instructors, and innovative thinkers who will challenge your assumptions, build your expertise, and prepare you for success.

Meet Our Faculty

Arrive prepared: summer math camp

Training in mathematics and statistics is a necessary component of the Simon PhD program coursework. In order to prepare you for success we offer three mini courses each summer before the official start of classes. Our Summer Math program is included in your full tuition scholarship. While not mandatory, these classes are strongly recommended and highly encouraged. Courses meet during the months of July and August.

Topic 1: Linear Algebra

This course provides an introduction to linear algebra. Topics include: Gaussian elimination; matrix operations; matrix inverses; vector spaces and subspaces; linear independence and the basis of a space; row space and column space of a matrix; fundamental theorem of linear algebra; linear transformations; orthogonal vectors and subspaces; orthogonal bases; Gram-Schmidt method; orthogonal projections; linear regression; determinants: how to calculate them, properties, and applications; LineCalculating eigenvectors and eigenvalues, basic properties; matrix diagonalization; application to difference equations and differential equations; positive definite matrices; tests for positive definiteness; singular value decomposition; classification of states, transience and recurrence, classes of states; absorption, expected reward; stationary and limiting distributions.

This course covers Optimization in Rn, Weierstrass Theorem, Unconstrained optimization, Lagrange Theorem and equality constraints, Kuhn-Tucker Theorem and Inequality constraints, Convexity, Parametric Monotonicity and Supermodularity.

This course teaches Random Variable, Distribution, Independence; Transformations and Expectations; Common Families of Distributions; Multiple Random Variables, and Markov Chains.