Master's Degree in Statistics
The Master of Science degree in Statistics combines theory and application. Our program emphasizes rigorous coursework in probability and mathematical statistics in addition to training in data analysis and computational methods. Graduates from the M.S. program may either directly enter the workforce as junior-level statisticians or continue their studies in pursuit of a more advanced degree.
4+1 Accelerated Master's Program
Current Tulane undergraduate students can earn this M.S. degree with just one additional year of study. This accelerated program allows you to count graduate-level courses toward both your B.S. and M.S. degrees, providing a fast track to an advanced credential.
What You'll Learn
A graduating master’s student in Statistics will have a strong foundation in probability, inference, data analysis, linear models, and computational methodology. The program emphasizes theoretical understanding, practical modeling skills, and modern computational tools used in applied statistics and data science.
Students design their coursework plan in consultation with the Director of Graduate Studies (DGS) and the Graduate Studies Committee (GSC) to ensure timely progress and compliance with School of Science and Engineering (SSE) policy.
Research
While the Statistics MS is a non-thesis program, students have opportunities to engage in research and independent projects under faculty mentorship. Students can enroll in MATH 7980 Reading and Research (1-9 credit hours), a semester-long project completed under faculty supervision, typically during their final semester of study. This option allows students to investigate topics of interest in probability, statistics, or computational methodology while developing research skills and deepening their expertise. Faculty research spans theoretical statistics, applied data science, biostatistics, machine learning, and quantitative analysis, providing diverse opportunities for students to engage with cutting-edge problems. Through coursework, independent projects, and faculty mentorship, you'll develop the analytical and computational skills essential for advanced careers in statistics and data science.
Meet Our Faculty
Our statistics faculty are accomplished researchers and educators with expertise spanning probability theory, mathematical statistics, linear models, computational statistics, biostatistics, and related areas. Faculty members are actively engaged in research investigating theoretical foundations of statistics and developing applications in data science, machine learning, and quantitative analysis. They are committed to mentoring graduate students through personalized course planning and research guidance, working with each student to design a program aligned with their academic interests and career goals. Whether through advanced coursework, independent projects, or reading and research seminars, our faculty create an environment where you'll develop both rigorous theoretical understanding and practical expertise in modern statistical methodology.
Curriculum Requirements
The 30-credit-hour Statistics MS is a non-thesis program requiring ten courses at the 6000/7000 level, including three core courses in probability theory, mathematical statistics, and linear models. Students design their coursework plan in consultation with the Director of Graduate Studies and the Graduate Studies Committee, selecting seven electives from options including stochastic processes, time series analysis, data analysis, optimization theory, and approved biostatistics courses. All students must maintain a minimum 3.0 GPA, with strict grade requirements: one B– triggers probation consideration, while two B– grades or any grade below B– results in probation and possible dismissal. Upon completion of core coursework, students take a four-hour written examination covering probability, linear models, and statistics (with two attempts allowed), though students with at most one B– grade may graduate without taking the exam. Up to 6 transfer credit hours may be applied toward the degree, with additional transfer credits possible with Graduate Studies Committee approval.
Career Benefits and Pathways
Graduates of the M.S. in Statistics program are well prepared for professional careers in data science, analytics, biostatistics, actuarial science, and quantitative research across government, healthcare, pharmaceuticals, consulting, and technology sectors. The program's emphasis on statistical modeling, data-driven decision-making, and computational skills positions graduates for technical roles where rigorous quantitative analysis is essential. Many graduates pursue doctoral studies in statistics, biostatistics, machine learning, econometrics, or related quantitative disciplines, leveraging their master's degree as a foundation for advanced research and academic careers. Whether entering the workforce immediately or continuing to doctoral study, graduates are equipped with the analytical expertise and technical skills demanded by organizations seeking to extract insights from data and make evidence-based decisions.
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FAQs
Statistics is the mathematical science of collecting, analyzing, and interpreting data to understand patterns, test hypotheses, and make evidence-based decisions under uncertainty. It combines probability theory, mathematical inference, and computational methods to extract meaningful insights from data and quantify the reliability of conclusions across diverse fields, from science and medicine to business and policy.
Applicants must hold a bachelor’s degree in mathematics, statistics, data science, or a closely related discipline. Admission is competitive and based on academic preparation, GPA, and foundational coursework in probability, calculus, and linear algebra.
Minimum Requirements:
1. GPA of 3.0 or higher (on a 4.0 scale).
2. Evidence of preparation in:
- Calculus and Multivariable Calculus
- Linear Algebra
- At least one course in probability or mathematical statistics (recommended)
Application Materials:
-Transcripts from all colleges/universities attended
-Personal statement outlining academic interests and goals
-At least one letter of recommendation (optional but encouraged)
4+1 Tulane Applicants:
-Minimum 3.5 GPA and adequate preparation in statistics and mathematics coursework
Applications are submitted online through the Tulane Graduate Application System. For questions regarding admission, applicants should contact Ilianna H. Kwaske, ikwaske@tulane.edu; for academic inquiries regarding the M.S. Programs in Applied Mathematics, please contact Prof. Rafal Komendarczyk, rako@tulane.edu.
Contact Us
Ilianna H. Kwaske, Ph.D.
Associate Dean for MS Programs & Professional Education
ikwaske@tulane.edu
Sarah Berry
Director of MS and Certificate Programs
smccarty@tulane.edu
Rosie Chavez
Director of Graduate Programs
rchavez@tulane.edu
Michelle Lacey
Mathematics Department Chair & Professor
mlacey1@tulane.edu