Statistics, M.S.

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The plan in Statistics leads to a Master of Science degree. This plan emphasizes statistical methods, statistical computing and data-analytic skills in two specializations: Applied Statistics and Big Data Analytics. It is suitable for students with an undergraduate background in mathematics, engineering, sciences or any background with solid mathematics and who wants to pursue a career as a statistician or a data scientist. Applicants will be considered if their preparation includes an adequate number of mathematics credits. In some cases, additional preparatory courses may be required.

Degree Requirements

Basic Preparation

MATH 2318 - Linear Algebra Credit Hours: 3
MATH 2413 - Calculus I Credit Hours: 4
MATH 2414 - Calculus II Credit Hours: 4
STAT 4344 - Introduction to Probability Credit Hours: 3
STAT 4345 - Introduction to Statistics Credit Hours: 3

Core Requirements (15 hours)

Statistics Core Requirements (15 hours)

The following five courses or their approved substitutes are required:

STAT 5432 - Principles of Statistical Inference Credit Hours: 3
STAT 5531 - Multivariate Statistical Analysis Credit Hours: 3
STAT 5532 - Linear Models and Regression Analysis Credit Hours: 3
STAT 5533 - Statistical Computing Credit Hours: 3
STAT 5535 - Experimental Designs and Analysis Credit Hours: 3

Applied Statistics Specialization (15 hours)

Thesis Option (15 hours)

STAT 6939 - Master’s Thesis Research Credit Hours: 3
STAT 5538 - Categorical Data Analysis Credit Hours: 3

Additional Information

Students take STAT 6939 for six credit hours, and six credit hours of 5000-6000 level approved electives.

Capstone Project (15 hours)

Six credit hours of 5000 - 6000 level of approved electives:

STAT 5538 - Categorical Data Analysis Credit Hours: 3
STAT 6837 - Statistics Research and Consulting I Credit Hours: 3
STAT 6838 - Statistics Research and Consulting II Credit Hours: 3

Additional Information

Students take STAT 6837 /STAT 6838 for 6 credit hours during the last 15 hours of course work.

Big Data Analytics Specialization (15 hours)

Capstone Project (15 hours)

Three credit hours of STAT 6838 , three credit hours of STAT 5537 or STAT 5634 , and nine credit hours from the following list:

DASC 5133 - Introduction to Data Science Credit Hours: 3
DASC 5333 - Database Systems for Data Science Credit Hours: 3
DASC 5335 - Deep Learning Credit Hours: 3
DASC 5433 - Big Data Credit Hours: 3
CSCI 5532 - Pattern Recognition and Image Processing Credit Hours: 3
CSCI 5833 - Data Mining: Tools and Techniques Credit Hours: 3
CSCI 5832 - Financial Data Mining Credit Hours: 3
CSCI 5933 - Computational Bioinformatics Credit Hours: 3

Additional Information

This specialization requires knowledge of Python programming and a database course such as CSCI 4333 - Design of Database Systems . Please note: CSCI 4333 has prerequisites that must also be completed.