MS in Statistics

Program of Study

The MS program offers a combination of mathematical statistics, applied statistics, and statistical computing tailored to each student. Before signing up for classes, all new students must meet with the MS program director to design an individualized program of study. The program of study should take into account the student's strengths and deficiencies, as judged by transcripts and interviews. It is recommended that students continue to seek advice throughout their time in the program.

Students must also maintain a minimum cumulative grade point average of B (3.0) in all coursework.

Curriculum

The MS program offers a combination of theoretical, applied, and computational statistics courses tailored for each student. The MS program requires 30 credits, no qualifying exam; and students can select from the following tracks or courses based on their interests in consultation with faculty advisors.

Biostatistics Track: Data Analysis, Applied Linear Models, Longitudinal Data Analysis, Survival Analysis, Categorical Data Analysis, Statistical Methods in Bioinformatics and Computational Biology, Clinical Trials, Causal Inference, Network Analysis

Computational Track: Fundamentals of SAS Programming for Data Management, Methods of Statistical Computing, Statistical Data Mining, Modern Regression Analysis, Statistical Methods in Bioinformatics and Computational Biology, Statistical Machine Learning, Statistical Deep Learning, Natural Language Processing

Applied Track: Data Analysis, Applied Linear Models, Applied Multivariate Analysis, Design of Experiments, Bayesian Statistics, Longitudinal Data Analysis, Categorical Data Analysis, Modern Regression Analysis, Statistical Consulting, Legal Statistics, Sample Surveys, Causal Inference, Fundamentals of SAS Programming for Data Management

Theoretical Track: Mathematical Statistics, Linear Models, Bayesian Statistics, Probability, Distribution Theory, Advanced Probability, Nonparametric Inference, Advanced Statistical Theory, Multivariate Analysis, Stochastic Processes

Credit Transfers

MS in Statistics students may transfer up to two approved courses (six credits) taken outside the Statistics Department for credit, but they must be in related fields (e.g., economics, management and finance, computer science, engineering, mathematics, or public health). To begin the transfer process, students should talk to their advisors and complete the Graduate Student Transfer Credit Request Form available on the CCAS website. The CCAS Master’s Student Handbook also provides additional guidance and information on degree requirements.

Pre-approved Courses Outside the Department

• CSCI 6212: Design & Analysis of Algorithms
• CSCI 6364: Database Management Systems
• CSCI 6442: Database Systems II
• CSCI 6907: Big Data & Analytics
• ECON 8375: Econometrics I
• ECON 8377: Econometrics II
• FINA 6223: Investment Analysis & Portfolio Mgmt
• MATH 6201: Real Analysis I
• MATH 6202: Real Analysis II
• MATH 6230: Complex Analysis
• MATH 6441: Introduction to Financial Math
• MATH 6620: Graph Theory
• MKTG 6243: Marketing Research
• PUBH 6002: Biostatistical Applications for Public Health
• DATS 6102: Introduction to Data Science
• DATS 6102: Data Warehousing

If desired, a student may complete the MS in Statistics program prior to admission to the Ph.D. program, in which case no more than 24 credit hours from the MS degree may be applied to the Ph.D. requirements. In this instance, the student will be required to take a minimum of 27 additional credit hours of coursework. The distribution of these courses between statistics and public health would depend on the nature of the master's degree and whether the transferred credit hours would be used to defray statistics or public health coursework. Full information is available in the online Graduate Admissions Application.

Optional Thesis

All candidates for the MS in Statistics complete at least 30 units of graduate credit approved for the department. For students who wish to complete a thesis, the department can approve a program of study consisting of 24 credit hours of coursework plus a thesis.

• Non-thesis option: Students complete six credits in required courses and 24 credits in elective courses for a total of 30 credits.
• Thesis option: Students complete six credits in required courses (STAT 6998-6999), six thesis credits, and 18 credits in elective courses, also totaling 30 credits.

All master’s theses must be supervised by a director plus one reader. It is up to the student to find a topic and a thesis advisor. Ask your academic advisor for guidance. Only after you find a thesis advisor you should register for STAT 6998 and STAT 6999 over two semesters.

Course Requirements

Prerequisites: These courses (or equivalents) are prerequisites for admission consideration and MUST appear on your transcript. Submit your MS in Statistics program application only after you have completed all of the required courses.

• MATH 1231: Single-Variable Calculus I (3 credits)
• MATH 1232: Single-Variable Calculus II (3 credits)
• STAT 2118: Regression Analysis (3 credits)

General prerequisite: coursework in multivariate calculus, matrix theory, and at least two undergraduate statistics courses.

The following requirements must be fulfilled:

The general requirements stated under Columbian College of Arts and Sciences, Graduate Programs.

30 credits. For the non-thesis option—6 credits in required courses and 24 credits in elective courses. For the thesis option—12 credits in required courses, including 6 credits in thesis, and 18 credits in elective courses. Students must have departmental approval to pursue the thesis option.

Required

• STAT 6201Mathematical Statistics I
• STAT 6202Mathematical Statistics II
• For students pursuing the thesis option:
• STAT 6999Thesis Research (taken twice for a total of 6 credits)

Electives

For the non-thesis option: 24 credits in electives, at least 18 of which must be in STAT courses. For thesis option: 18 credits in electives, at least 12 of which must be in STAT courses.

Elective courses outside statistics may be taken in related fields, such as economics, mathematics, finance, management, computer science, engineering, public health, and data science.