Program Overview

Introduction to the Department of Statistics and Probability

The Department of Statistics and Probability (STT) at Michigan State University is a dynamic community of scholars dedicated to addressing significant theoretical challenges and pressing real-world problems. The department prioritizes imparting expertise to meet the educational needs of graduate students, nurturing the future generation of statisticians.

Graduate Program Overview

The graduate program is committed to delivering a robust, rigorous, and high-quality curriculum that equips students for successful careers in industry, academia, government, and public service. The department has a longstanding tradition of prioritizing theory, ensuring that graduate students receive rigorous training. The program encompasses a wide array of contemporary statistical domains, including methodology, computation, and practical applications, while upholding rigorous standards.

Program Focus

The department takes great pride in its unwavering focus on probability theory and stochastic processes, setting it apart as the only department in the country with 'probability' in its name. Students receive world-class training in both theory and methodology, making them highly sought after by top industrial employers and prestigious academic departments.

Career Opportunities

Graduates of the program are well-equipped for successful careers in various fields, including:

• Industry
• Academia
• Government
• Public service

Program Details

The department offers M.S. and Ph.D. programs, providing students with invaluable skills that will benefit their career and intellectual growth for a lifetime. The MSU Graduate School provides a public demographic description of the PhD program, along with information on:

• Percent of applicants admitted
• Degree completion
• Time to degree
• Placement data

Research Areas

The department's research pursuits include a wide range of contemporary statistical domains, such as:

• Methodology
• Computation
• Practical applications
• Probability theory
• Stochastic processes