M.S. in Applied Statistics

What is Applied Statistics?

Statistics is the science of data with a principled foundation in mathematics that has applications in many fields such as social sciences, engineering, business, biomedical sciences and economics.

Program Description

The M.S. in Applied Statistics is intended for students who have a mathematical background (not necessarily having a degree in mathematics or statistics) that want to develop strong data analytic skills to solve complex, real world problems.

This program meets the needs for:

  • recent graduates who are seeking a master’s degree program in applied statistics
  • mid-career workers with a solid mathematics and/or statistics background who are seeking a graduate program either for career advancement or for a career change.
  • part time evening study while working full-time.

Program coursework includes modern data topics in Regression Analysis, Computational Probability, Statistical Simulation, Design of Scientific Experiments, and Statistical Machine Learning that are desirable in industry and academia.

In addition to coursework, students also take a statistical consulting practicum course. The practicum is intended to give students practical, hands-on statistical consulting training. The M.S. in Applied Statistics program produces graduates who can deal with big data, perform statistical analysis to detect hidden patterns in data, perform risk factor analysis and perform predictive analysis.

Students may pursue the degree on a full-time or part-time basis. Many evening courses are offered.

apply to the applied statistics program

Full time dedicated students can complete the degree in as little as 12 months.

Program Resources

Graduate Bulletin

For a thorough program description, which includes program requirements and courses, please visit the Graduate Bulletin.

Program Coursework

A master’s student is admitted to the non-thesis program (Plan B), which requires at least 30 credit hours of coursework with at least 15 credit hours at the 6000-level and a required 3 credit practicum. A formal request to pursue a thesis (Plan A) must be approved by the department’s Graduate Committee and the Graduate School.  The Plan A student must complete a minimum of 30 credit hours including 6 hours of thesis credit.  At least 12 credit hours must be at the 6000-level. All master’s students in Applied Statistics must complete the 21-credit core:

MSSC 5710 Mathematical Statistics 3 credits
MSSC 5780 Regression Analysis 3 credits
MSSC 6010 Computational Probability 3 credits
MSSC 6020 Statistical Simulation 3 credits
MSSC 6040 Applied Linear Algebra 3 credits
MSSC 6240 Design and Analysis of Scientific Experiments 3 credits
MSSC 6250 Statistical Machine Learning 3 credits

To complete the remaining credit hours, master’s students may select additional approved coursework within MSSC or from outside departments. 

The following are the approved courses within MSSC:

MSSC 5540 Numerical Analysis 3 credits
MSSC 5630 Mathematical Modeling and Analysis 3 credits
MSSC 5700 Theory of Probability 3 credits
MSSC 5750 Computational Statistics 3 credits
MSSC 5760 Time Series Analysis 3 credits
MSSC 5790 Bayesian Statistics 3 credits
MSSC 5931* Topics in Mathematical or Statistical Sciences 3 credits
MSSC 6000 Scientific Computing 3 credits
MSSC 6030 Applied Mathematical Analysis 3 credits
MSSC 6210 Theory of Statistics 3 credits
MSSC 6230 Multivariate Statistical Analysis 3 credits
MSSC 6931* Topics in Mathematical or Statistical Sciences 3 credits
MSSC 6960* Seminar in Mathematical or Statistical Sciences 1-3 credits
MSSC 6995* Independent Study in Mathematical or Statistical Sciences 1-3 credits

* To be preapproved. Different topics and seminar courses are offered.

The following are tentatively approved courses from outside MSSC:

BIEN 6200 Biomedical Signal Processing 3 credits
BIEN 6220 Multidimensional Biomedical Time Series Analysis 3 credits
BIIN 6000 Introduction to Bioinformatics 3 credits
BUAD 6112 Skills: SAS 1 credit
BUAD 6113 Skills: SPSS 1 credit
BUAD 6160 Business Analytics Using Spreadsheets 3 credits
CHEM 5230 Forensic Chemistry 3 credits
CHEM 6403 Statistical Thermodynamics 3 credits
COSC 5500 Visual Analytics 3 credits
COSC 5610 Data Mining 3 credits
COSC 6060 Parallel and Distributed Systems 3 credits
ECON 6560 Applied Econometrics 3 credits
ECON 6561 Applied Time-Series Econometrics and Forecasting 3 credits
EECE 6020 Probability and Random Processes in Engineering 3 credits
EECE 6340 Stochastic Systems Estimation and Control 3 credits
EECE 6510 Optimal and Adaptive Digital Signal Processing 3 credits
EECE 6540 Digital Image Processing 3 credits
EECE 6830 Pattern Recognition 3 credits
EECE 6840 Neural Network and Neural Computing 3 credits
MEEN 5360 Intermediate Thermodynamics 3 credits
MEEN 5410 Experimental Design 3 credits
MEEN 6330 Statistical Thermodynamics 3 credits
MEEN 6470 Statistical Methods in Engineering 3 credits
PHYS 5012 Quantum Mechanics 3 credits
PHYS 5062 Introduction to Thermodynamics 3 credits
PSYC 6135 Single Subject Research Methods 3 credits
PSYC 8101 Advanced Statistics and Design 1 3 credits
PSYC 8102 Advanced Statistics and Design 2 3 credits

Please consult with the applied statistics program director to ensure this list is current.

Program Learning Outcomes

Upon graduation from the APST program, students will be able to:

  1. Demonstrate solid foundations of probability, statistics and mathematical computational tools.
  2. Show proficiency in analyzing real and complex data to detect patterns and perform predictive analysis.
  3. Use coursework learning to solve real data problems and demonstrate communication skills in presenting the results.

Why Major in Applied Statistics?

Suggested Curriculum

Course Advising

Student Success Stories

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Contact Us

For more information, please contact Dr. Mehdi Maadooliat.