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Degree Requirements
Required courses for a student’s chosen option are listed below, as is the 
MPS Application to Graduate
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Both options require a total of at least 30 credit hours in required courses and electives. The 30 required credit hours must be earned while an MPS student is enrolled in the MPS program; no transfer of credits from undergraduate study or another graduate program, etc. is allowed.
- All required course must be taken for a letter grade, except STSCI 5953 which is S/U only.
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At most one elective course to be used toward the MPS degree can be taken S/U each semester.
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Option I students can take STSCI 5045, STSCI 5060, and STSCI 5065 as electives and so may take these courses S/U. For option II students, these courses are required and must be taken for a letter grade.
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A grade of C- or better (or S for S/U courses) is required of all courses used to meet MPS requirements.
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A GPA of 2.5 or higher in courses used toward the MPS degree is required for graduation.
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Electives must be taken from the list below of approved electives.
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Students can ask the Director of the MPS Program to add courses to the list of electives. With few exceptions, to be approved by the Director, a course must be a technical course numbered 5000 level or higher and have substantial statistics content.
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Two courses covering similar material and at the same level cannot both be used toward the 30 credit hours for the MPS degree.
Core Required Courses
STSCI 5030: Linear Models with Matrices (4 credits)
STSCI 5080: Probability Models and Inference (4 credits)
STSCI 5953: MPS Career Management (1 credit)
STSCI 5954: Project Development & Professional Communication (2 credits)
STSCI 5999: Applied Statistics MPS Data Analysis Project (4 credits)
Additional Required Courses for Option II
STSCI 5045: Python Programming and its Applications in Statistics (3 credits)
STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits)
STSCI 5065: Big Data Management and Analysis (3 credits)
Statistical Science Electives
Option I students must take at least 12 credit hours and Option II students at least 4 credits of Statistical Science electives from this list. Option II students cannot use STSCI 5045, 5060, or 5065 as a statistical science elective since these courses are required as core option II courses.
STSCI 5010: Applied Statistical Computation with SAS (4 credits)
STSCI 5040: R Programming for Data Science (4 credits)
STSCI 5045: Python Programming and its Applications in Statistics (3 credits)
STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits)
STSCI 5065: Big Data Management and Analysis (3 credits)
STSCI 5090: Theory of Statistics (4 credits)
STSCI 5100: Statistical Sampling (4 credits)
STSCI 5140: Applied Design (4 credits)
STSCI 5160: Categorical Data (4 credits)
STSCI 5550: Applied Time Series Analysis (4 credits)
STSCI 5600: Integrated Ethics in Data Science (3 credits)
STSCI 5630: Operations Research Tools for Financial Engineering (3 credits)
STSCI 5640: Statistics for Financial Engineering (4 credits)
STSCI 5740: Data Mining and Machine Learning (4 credits)
STSCI 5750: Understanding Machine Learning (4 credits)
STSCI 5780: Bayesian Data Analysis: Principles and Practice (4 credits)
STSCI 6070: Functional Data Analysis (3 credits)
STSCI 6520: Computationally Intensive Statistical Methods (4 credits)
STSCI 6780: Bayesian Statistics and Data Analysis (3 credits)
Other Approved MPS Electives
AEM 7100: Econometrics I (3 credits)
BTRY 6381: Bioinformatics Programming (3 credits)
BTRY 6830: Quantitative Genomics and Genetics (4 credits)
BTRY 6840: Computational Genetics and Genomics (4 credits)
CS 5780: Machine Learning (4 credits)
CS 5786: Machine Learning for Data Science (4 credits)
ORIE 5510: Introduction to Engineering Stochastic Processes I (4 credits)
ORIE 5580: Simulation Modeling & Analysis (4 credits)
ORIE 5581: Monte Carlo Simulation (2 credits)
ORIE 5600: Financial Engineering with Stochastic Calculus I (4 credits)
ORIE 5610: Financial Engineering with Stochastic Calculus II (4 credits)
ORIE 5741: Learning with Big Messy Data (4 credits)
ORIE 6500: Applied Stochastic Processes (4 credits)
ORIE 6741: Bayesian Machine Learning (3 credits)
Application to Graduate
Last updated: March 11, 2022