The School of Math together with the School of Industrial and Systems Engineering offer graduate work leading to the Master of Science in Statistics. The emphasis in this joint program is on statistics as a science applicable in a technological environment. Although this program can lead to further work toward a Doctoral degree in Applied Statistics, Mathematics, or Bioinformatics, it is designed Primarily to provide the background for a successful professional career in statistics.
Career fields for graduates of this program may be found in all areas of research, industry, and government. The program, which can be completed in twelve months, is designed to provide the graduates with competence in the collection, analysis, and interpretation of data and a sound understanding of statistical principles. Students work with faculty actively engaged in research and prepared to teach the latest developments in statistics.
The program is quite flexible with regard to students' background. In particular, anyone interested in statistics who holds or anticipates an undergraduate degree in engineering, mathematics, or science is encouraged to apply. It is important, however, that all prospective students' previous coursework include (1) a multivariate calculus course, (2) a calculus based probability course, (3) a linear algebra course, and (4) familiarity with some computer programming language.
The core courses for the MS in Statistics degree are taken in Math and in ISyE. Choices of the remaining courses in the program are quite flexible; students can concentrate their studies on a specific area of application such as Operations Research, Psychology, Mechanical Engineering, etc., or, in preparation for the PhD, can take more mathematical courses. The MS degree in Statistics is awarded upon successful completion of the courses in the program as described below according to the stipulations of the Institute catalog. Electives are to be chosen in consultation with a faculty member.
|Statistics Electives:||15 hrs|
|Free Electives:||3 hrs|
Math 4317 Real Analysis
Math 6262 Statistical Estimation
Math 6263 Testing Statistical Hypotheses
Math 6266 Linear Statistical Models
Math 6267 Multivariate Statistical Analysis
ISyE 6402 Time-Series Analysis
ISyE 6404 Nonparametric Data Analysis
ISyE 6405 Statistical Methods for Manufacturing Design and Improvement
ISyE 6412 Theoretical Statistics
ISyE 6416 Computational Statistics
ISyE 6420 Bayesian Statistics
BME/ISyE 6421 Biostatistics
Math / ISYE 6761 Stochastic Processes I
Math / ISYE 6762 Stochastic Processes II
Math/ISyE 6781 Reliability Theory
Math/ISyE 6783 Financial Data Analysis
ISyE 6810 System Monitoring and Prognostics
ISyE 7400 Advanced Design of Experiments
ISyE 7401 Advanced Statistical Modeling
ISyE 7405 Multivariate Data Analysis
ISyE 7406 Data Mining
ISyE 7441 Theory of Linear Models
Affiliated SoM Faculty
- Christian Houdré - Nonparametric statistics; Statistical methods in finance and bioinformatics (Professor, Ph.D., McGill University)
- Vladimir Koltchinskii - Probability theory; mathematical statistics (Professor, Ph.D., Kiev University)
- Cheng Mao - Mathematical statistics; Machine learning theory; Applied probability (Assistant Professor, Ph.D., MIT)
- Mayya Zhilova - Mathematical statistics; Statistical learning theory; Uncertainty quantification (Assistant Professor, Ph.D., Humboldt University)
- ISYE Affiliated Faculty
Specific questions about the program may be directed to Professor Vladimir Koltchinskii. General questions about admission into the School of Math should be sent to the School's Director of Graduate Studies. Questions about admission into the School of Industrial and Systems Engineering should be directed to the ISyE's Associate Chair for Graduate Studies.