Degree Requirement
The general requirement for the master’s degree in Statistics is to complete 37 credits of courses, including 10 credits of required courses, at least 21 credits of elective courses in the Department of Statistics, and at most 6 credits of elective courses in other departments. In each semester, the graduate students must take at least three courses, but no more than 15 credit hours. The graduate student is also required to present the results of the written report in the oral exam, and to submit a thesis to the Department.
Course Information
Required Courses
M0303 Statistical Theory (3/3): 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.
M0798 Statistical Consulting (3): To train graduate students with the experience of processing real data sets and the ability of using statistical methodologies for practical applications.
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
M0115 Multivariate Analysis (3): This course introduces fundamental concepts of analyzing multivariate data, including basic multivariate statistical inferences, principal components analysis, factor analysis, canonical correlation analysis, classification and cluster analysis etc. In addition to basic ideas and theoretical results, practical applications are also illustrated by examples.
M0189 Sampling Theory (3): This course covers concepts of sampling survey, major sampling designs and its estimation procedures, and evaluation of precision of a sampling design.
M0202 Quality Control (3): The course is concerned with how to use modern statistical methods for quality control and improvement including the subjects from basic principles to state-of-the-art concepts and applications. The objective is to give the students a sound understanding of the principles and the basis for applying them in a variety of situations.
M0264 Time Series (3): An introduction to time series and its application.
M0880 Applied Linear Model (3):The linear model involves the simplest and seemingly most restrictive statistical properties: independence, normality, constancy of variance, and linearity. However, the model and the generalized model are very versatile and robust. The linear model is a very important training of any statistician, applied or theoretical.
M0883 Statistical Computing(3):This course focuses on the design of statistical software including special techniques for probability distributions, methods for generating random variables, numerical methods for linear models and multivariate data, bootstrap, jackknife, and some other recently developed methods.
M1043 Survival Analysis (3):This course will introduce how to analyze the time-to-event data by statistical methods of survival analysis. In addition to basic ideas and theoretical results, practical applications of statistical software will be also demonstrated by biological and medical examples.
M2615 Artificial Intelligence for Business Application (3):Data analysis becomes a mainstream trends that companies are vying to master. The teaching goal is to apply the theoretical methods of big data analysis and apply to real-world data. Our student have ability to solve real problems independently. The teaching methods and case methods provided allow students to understand the concepts of data analysis and data preparation, methods and demonstrations of data mining, and advanced data mining applications. Students can easily apply data mining methods through Python. , Thereby enhancing big data analysis and digital decision-making capabilities.
M2616 Financial Big Data Analysis (3):Fintech is the main axis of the current financial industry, the main purpose of this course is to apply large data analysis in the financial data. This course is divided into four parts: A. introduces the financial industry data systems; B. introduces financial Analytical algorithms and machine learning; C. introduces the application of big data in the banking, securities, and insurance industries; D. the application of big data in credit investigation. Through the course, students can understand the methods of big data analysis application in the financial industry.