Classification : Requisite   Class Number  :  326.212   Credit : 3   Grade : 1

This course introduces basic concepts of computer programming and computer-assisted statistical data anslysis. We begin with learning how to write a program using general-purpose programming languages such as C and Fortran, and then study languages specialized for statistical data analysis, such as R. We will examine various statistical analysis methods using these programming languages. Optionally, we will study the elementary concepts of database.

Classification : Requisite   Class Number  :  326.211   Credit : 3   Grade : 2

This course is designed to introduce basic probability concepts, theories and their applications to related fields such as natural science, engineering, and social science.
Classification : Elective   Class Number  :  326.214   Credit : 3   Grade : 2
This course treats the theory and practice of sampling. It focuses on sampling and surveying of finite populations from various points including the simple random sample, stratified, cluster, and double sampling, properties of various estimators including ratio and regression, sampling with unequal probabilities, and error estimation for complex samples. Students will be required to perform survey practices and participate in group discusssions.

Classification : Requisite   Class Number  :  326.311   Credit : 3   Grade : 3

This course focuses on conditional probability, stochastic independence and the distributions of random variables such as Normal, Binomial, Multinomial, Gamma, Chi-square, Poisson, and Multivariate Normal variables.

Classification : Requisite   Class Number  :  326.312   Credit : 3   Grade : 3

This advanced course provides a deeper understanding of limit distributions,including the central limit theorem, statistical estimation, testing statistical hypotheses, nonparametric tests, sufficient statistics, statistical inferences and normal theory. This course has a prerequiste of Mathematical statistics 1.

Classification : Requisite   Class Number  :  326.313   Credit : 3   Grade : 3

This course deals with both the theory and application of regression analysis covering simple, multiple, and nonlinear regression analysis, dummy variables, response surface analysis, selection of variables and diagnostics. Students will be required to perform statistical analysis using SAS.
Classification : Elective   Class Number  :  326.314   Credit : 3   Grade : 3
This course introduces categorical data analysis based on log-linear model, selection of models, goodness-of-fit test, maximum likelihood estimation of expected frequencies in the contingency table, analysis of incomplete contingency tables, logit models, and linear logistic regression models.
Classification : Elective   Class Number  :  326.315   Credit : 3   Grade : 3
This course introduces Latin and Graeco-Latin square, factorial and block design, mixed models, fractional replication and complete randomization of one factor. Regression analysis and Lab are prerequisites to this course.
Classification : Elective   Class Number  :  326.316   Credit : 3   Grade : 3
The focal point of this course is on multivariate data and its analysis. The class will estimate and test the means of multivariate data, perform principal component analysis along with factor analysis and cluster as well as discriminant analysis. The course has prerequistes of Mathematical statistics 1, 2, and Linear algebra.
Classification : Elective   Class Number  :  326.411   Credit : 3   Grade : 4
This course deals with subjective probability, preferences quantification, Bayesian decision theory, conjugate prior distribution, limit posterior distribution, Bayesian estimation and test, and secondary decision theor.
Classification : Elective   Class Number  :  326.412   Credit : 3   Grade : 4
In this course, students will study the estimation and testing of survival time and be introduced to the life table method and Kaplan-Meyer estimation to model survival functions. Topics will include various test methods for the comparison of three or more groups as well as regression models such as Cox proportional hazard models and accelerated regression models for the selection of risk factors that affect survival time.
Classification : Elective   Class Number  :  326.413   Credit : 3   Grade : 4
This course covers basic concepts and methodologies of data mining on various real problems. Preprocessing procedures including categorization, sampling etc are taught and various data mining methods including linear regression, logistic regression, decision trees, neural networks, clustering and association are covered. also, evaluation methods such as lift and prediction errors are taught. Finally, as a term project, students are participated in one real project. In this course, various statistical packages such as R, SPSS, SAS are extensively used.
Classification : Elective   Class Number  :  326.414   Credit : 3   Grade : 4
This elementary course introduces basic nonparametric methods and distribution-free statistics. It also deals with distributions of order and rank statistics. Some of the specific issues that are dealt with include nonparametric estimation of point and confidence intervals with comparison of parametric methods, location parameter estimation of one sample, location and scale parameter estimation of two samples, and nonparametric testing problem of distribution functions.
Classification : Elective   Class Number  :  326.415   Credit : 3   Grade : 4
This course introduces the different laws and uses of various statistical packages. Topics include the moving average, exponential smoothing, the ARIMA models and the basic concepts of seasonal effects.
Classification : Elective   Class Number  :  326.416   Credit : 3   Grade : 4
This course deals with theory of statistical quality control, covering normal plot, control chart, sampling inspection, probability theory, and single sampling of measurement. The courses Statistics and Laboratory, Mathematical Statistics 1, 2, and Sampling Design and Survey Practice are prerequisite.
Classification : Elective   Class Number  :  326.418   Credit : 3   Grade : 4
This course deals with nonparametric estimation methods for functions in various statistical models and is mainly focused on methodologies and applications rather than on theories. Topics that we will examine in this course include the following: nonparametric estimation methods such as Kernel estimation, local polynomial method, wavelet estimation and spline estimation; estimation methods of density function, regression function, survival function and quantile function. We will also observe the ways in which these methods can be applied to classification and discriminant analysis, generalized linear model, censored regression model, and proportional hazard model.
Classification : Elective   Class Number  :  M0000.000500   Credit : 3   Grade : 4
In this course, discrete and continuous Markov chain and renewal process are covered. In the Markov chain, recurrence, Ergodic theorem, reversibility, and their applications are main subject. In the renewal process, several renewal theorem and their applications are covered.

Classification : Elective   Class Number  :  M1399.000100   Credit : 3   Grade : 4

This course offers basics of statistical computing methods for parametric and Bayesian statistics. For parametric statistics, we study optimization methods such as the Newton-Raphson method, for maximizing likelihood functions. For Bayesian statistics, we study Markov-chain Monte Carlo methods such as Gibbs Sampling and the Metropolis algorithm. Besides theory, we also perform real data analysis using these methods. We also introduce data structures and matrix algorithms useful for computational statistics.

Classification : Elective   Class Number  :  M1399.001400  Credit : 3   Grade : 3

The course aims to provide students with an understanding and application of a variety of basic methods of data-driven research and analysis. This course intends to give an overview of various data analysis methods and approaches including refinement in problem solving, data collection and cleaning, exploratory data analysis, visualization, statistical inference and prediction, and decision-making. Also, students are expected to apply the methods to real data, experience the whole process of data analysis, and develop problem-solving skills.

Classification : Elective   Class Number  :  M1399.001500  Credit : 3   Grade : 4

This course aims to expand to data analysis with a complex structure to understand theoretical explanation and limitations of the methods and to develop the ability to apply the correct analysis method. Select an appropriate analysis method for new data, implement a methodology through software, and improve the ability to deliver results to non-data science majors. To this end, we combine topics and deal with various statistical methodologies. 1. Frequentism and Bayesian Decision Theory 2. Statistical hypothesis testing 3. Multiple testing method to control FWER and false discovery rate 4. Statistical inference using empirical Bayes 5. Confidence interval 6. Various machine learning techniques 7. General linear model 8. Dimensional reduction methods are included in the curriculum.

Classification : Requisite   Class Number  :  M1399.000900  Credit : 3   Grade : 3

Statistics is the science that provides a theoretical ground for making reasonable decisions based on data. This course deals with the concept of probability and properties of random variables, which are important basic concepts of statistical theory, and various types of statistical estimation and hypothesis testing. From this course, students will understand the data generating process based on probabilistic models, and establish a theoretical foundation of statistical inference for analyzing data.


Students who are minor in statistics can take the course. Students majoring in statistics and students who are double major in statistics are not allowed to take the course.