Learning Objectives

The department offers three graduate programs: a Ph.D. program, a M.S. program and a Master of Applied Statistics (MAS) program.

Part of the primary academic mission of the department is to educate our graduate students in both core and emerging statistical methodology. A hallmark of our graduate programs is a strong emphasis on collaborative, interdisciplinary research with subject matter areas. This emphasis is to help assure the scientific relevance and import of our student’s education and activities.

Our areas of strength are applied statistics, computational statistics and interdisciplinary research, including computer vision, statistical learning, computational biology/bioinformatics, social statistics, environmental statistics and experimental design.

The learning objectives of the three graduate programs are:

Doctor of Philosophy
The purpose of the Ph.D. program is to further develop knowledge and skills in Statistics and to demonstrate the ability to conduct independent research and analysis in Statistics. Through completion of advanced course work and rigorous skills training, the doctoral program prepares students to make original contributions to the knowledge of Statistics and to interpret and present the results of such research.

Master of Science
The purpose of the master’s program is to further develop knowledge and skills in Statistics and to prepare students for a professional career or doctoral studies. This is achieved through completion of advanced courses in Statistics as well as related areas, and experience with independent work and specialization.

Master of Applied Statistics
To prepare students for a professional career in applied statistics and data science with the following specific learning outcomes:

  1. Develop technical skills in probability modeling and statistical inference for the practical application of statistical methods in their current or future employment.
  2. Use existing and develop new statistical tools involving computer programming languages, such as R and SQL, for data science problems across different applied domains.
  3. Communicate and present statistical ideas clearly in oral and written forms using appropriate technical terms and deliver data analysis results to non-statistical audience.