Master of Science in Statistics

The Master of Science in Statistics program is designed to prepare students for both professional practice and advanced research by combining rigorous training in statistical theory with modern computational tools and applied perspectives. Students gain the analytical, methodological, and programming skills essential for addressing today’s complex data challenges.

Degree Requirements

To earn the Master of Science (M.S.) degree in Applied Statistics, students must complete at least 24 credits, including a minimum of 18 credits within the Department of Statistics and Data Science. The curriculum is organized into three core areas:

• Statistical Methods (9 credits): Provides a comprehensive grounding in statistical theory, modeling, and inference, enabling students to develop and apply rigorous methodologies for analyzing diverse types of data.
• Statistical Computing (6 credits): Training in programming, computational tools, and algorithms for large or complex datasets.
• Statistical Applications (9 credits): Practical application of statistical techniques to real-world problems across disciplines.

In addition, graduate students are required to take at least three courses each semester (with a maximum of 15 credit hours). They must also present the findings of a written report in an oral examination and submit a thesis to the Department.

By integrating theoretical foundations, computational expertise, and applied problem-solving, the program equips graduates for successful careers in statistics, data science, and applied research, as well as for doctoral-level study.

Course Information

Required Courses

M0303 Statistical Theory (3): This course focuses on the theoretical statistics. Topics include distribution theory, approximation to distributions, modes of convergence, limit theorems, statistical models, parameter estimation, comparison of estimators, confidence sets, theory of hypothesis tests, and Bayesian inference.

M0800 Business Ethics (1):  What other issues dose a business should attend to in addition to its “bottom line”? When a business operates globally, should it modify its ethical standard based on local laws and regulations? This course covers such questions and hopes to provide students with some generally accepted guidelines. Students will not only read about relevant theories in business ethics but also discuss various business ethics issues.

T0095 Seminar I (1/1):This course is organized to help graduate students to understand the most recent developments in different areas of statistical research by inviting few talks given by scholars in statistics. Students can give an oral presentation on the paper they chose which is highly related to their graduation thesis.

T0096 Seminar II (1/1): This course is organized to help graduate students to understand the most recent developments in different areas of statistical research by inviting few talks given by scholars in statistics. Students can give an oral presentation on the paper they chose which is highly related to their graduation thesis.

Elective Courses

• Statistical Methods: Design of Experiments (3), Applied Linear Models (3), Multivariate Analysis (3), Categorical Data Analysis (3), Sampling Theory (3), Functional Data Analysis (3), Time Series (3), Spatial Statistics (3), Quality Control (3), Operations Research (3), Nonparametric Regression (3), Quantile Regression (3), Special Topics in Bayesian Analysis (3).
• Statistical Computing: Statistical Computing (3), Statistical Computing and Simulation (3), Machine Learning (3), Deep Learning (3), Distributed Computing (3), Advanced Software Applications for Big Data (3).
• Statistical Applications: Statistical Consulting (3), Applications of Statistical Methods in Clinical Trials (3), Data Mining (3), Epidemiology (3), Biostatistics (3), Survival Analysis (3), Analysis of Censored Data (3), Reliability Analysis (3), Theory and Applications of Process Capability Indices (3), Financial Big Data Analysis (3), Artificial Intelligence for Business Application (3), Special Topics in Finance (3), Management Decision Analysis (3), Financial Econometrics (3), Survey Sampling Practice (3), Insurance Actuarial Science (3), Applications of R in Financial Econometrics (3), Financial Software Applications (3), Software Applications for Health Data (3), Advanced Applications of Artificial Intelligence in Biomedicine (1).