M.S. IN APPLIED STATISTICS

About the program

Applications of statistics are found in many fields such as social sciences, engineering, business, biomedical sciences and economics. 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:

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.

Prerequisites for admission

Admission to this program will be based on a sufficient formal mathematics and/or statistics background and a previous experience with programming. The Applied Statistics program can accommodate students from a wide variety of disciplines. These include undergraduate degrees in:

An undergraduate degree in the above fields is not necessary since the main criterion for admitting the students will be their ability to grasp complex mathematical ideas. A minimum mathematics requirement includes:

For example, a student with an undergraduate degree in economicsand grades of A or AB in calculus 1 and calculus 2 in addition to courses in economics and linear algebra may be suitable for the Applied Statistics program.

Coursework

A master’s student is admitted to the non-thesis program (Plan B), which requires at least 30 of course work 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:

MSCS 5710 Mathematical Statistics 3 credits
MSCS 5780 Regression Analysis 3 credits
MSCS 6010 Probability 3 credits
MSCS 6020 Simulation 3 credits
MSCS 6040 Applied Linear Algebra 3 credits
MSCS 6240 Design of Experiments and Data Analysis 3 credits
MSCS 6250 Advanced Multivariate Data Analytics 3 credits

To complete the remaining credit hours, master’s students may select additional approved coursework within MSCS or from outside departments.  The following are the approved courses within MSCS:

BIIN 6000 Introduction to Bioinformatics 3 credits
MSCS 5540 Numerical Analysis 3 credits
MSCS 5610 Data Mining 3 credits
MSCS 5630 Mathematical Modeling and Analysis 3 credits
MSCS 5700 Theory of Probability 3 credits
MSCS 5760 Time Series Analysis 3 credits
MSCS 5931 Topics in Math, Stat or Comp Sci: Topics in Data Sci. 3 credits
MSCS 6030 Applied Mathematical Analysis 3 credits
MSCS 6060 Parallel and Distributed Systems 3 credits
MSCS 6050 Elements of Software Development 3 credits
MSCS 6931* Topics in Mathematics, Statistics, and Comp. Sci. 3 credits
MSCS 6960* Seminar in Mathematics, Statistics, and Comp. Sci. 1-3 credits
* To be preapproved. Different topics and seminar courses are offered.

The following are approved courses from outside MSCS:

BUAD 6112 Skills: SAS 1 credit
BUAD 6113 Skills: SPSS 1 credit
BUAD 6160 Business Analytics Using Spreadsheets 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 6822 Machine Learning 3 credits
EECE 6830 Pattern Recognition 3 credits
EECE 6840 Neural Network and Neural Computing 3 credits

Application Requirements

Application Deadline

Rolling admission; this means you may apply any time before August 1 for fall term admissions (June 1 for international applicants) and December 15 for spring term admissions (October 15 for international applicants). These are the dates by which your applications must be complete, meaning that all required documentation must be received in the Graduate School by these dates.

However, applicants who wish to be considered for merit-based financial aid (graduate assistantships/fellowships), please be aware of the merit-based financial aid deadlines by which all applicant materials must be received by the Graduate School: Fall (August) Term: January 15, Spring (January) Term: November 15, Summer (May) Term: April 15.

Financial Aid

For a comprehensive listing of merit-based aid (graduate assistantships/fellowships) please visit the departmental financial aid web page. Private scholarships may also be available. U.S. citizens and permanent residents may be eligible to apply for need-based federal aid (loans) to help fund their educational expenses as well.

For more information, please contact Naveen Bansal.


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Summer Research Experience

The Department of Mathematics, Statistics and Computer Science hosts a Summer Research Experience (REU) for Undergraduates. This program provides undergraduates with an intensive, faculty-mentored, summer research experience in the areas of applied mathematics, high-performance computing, statistics, ubiquitous systems and mathematics education. Learn more