Advances in computer technology in recent decades have made it possible to use complex statistical models that were previously impossible. In this course, you will learn the basic concepts of computer programming for modern statistical analysis and the basics of statistical data analysis based on the R programming language.

Probabilistic methods are being used to understand almost all phenomena in modern society, not only in the natural sciences. Probability theory is an important branch of modern mathematics and has a wide range of applications in computer science such as artificial intelligence and computer communication. In this course, students will first understand the basic concepts of probability, and then study probabilistic thinking and approaches used in natural, engineering, and social sciences, and introduce the mathematical techniques required for them. It is also a good preparation for Mathematical Statistics 1.

This course considers basic sample design and estimation theory to cover diverse and practical designs. The main contents include simple random sampling, stratified sampling, cluster sampling, stratified multistage sampling, ratio and regression estimation, and non-sample error. Each method covers parameter estimation, sample size estimation, optimal sample allocation, and relative efficiency. Sample design and survey practice are conducted in the field as case studies.

This course introduces the definition of probability and the concepts of distribution functions and probability density functions, which are fundamental to statistics. You will be introduced to various types of distribution functions and their properties. Students will also learn about the concept of a sampling distribution, the distribution of statistic, various properties of sampling distributions and approximations to sampling distributions.

This course covers the basic theory of statistical inference, namely, estimation and testing. Estimation methods include method of moment, maximum likelihood estimation, Bayesian estimation, and minimum variance unbiased estimation, while testing methods include the likelihood ratio test, Rao test, Wald test, Bayesian test, and uniformly most powerful test. This course also covers the theoretical background of statistical inference which includes sufficient statistics, the Rao-Blackwell theorem, the Cramer-Rao inequality, derivation of asymptotic properties of estimators, and asymptotic approximation of tests.

This course basically deals with fitting linear regression models to datasets. It also covers statistical inference on model parameters. The topics include simple linear regression, multiple linear regression, checking model adequacy, weighted least squares, transformation, regression diagnostics, detection of leverage and influential observations, regression techniques for categorical variables, multicollinearity, ridge regression, variable selection, nonlinear regression, generalized linear models, artificial neural networks.

This course focuses on understanding and analysis of categorical data. Categorical data is widely collected in routine surveys and experiments in various fields such as social sciences and public health. Students will learn a variety of analysis methods, including the analysis of contingency tables, generalized linear models, logistic regression models, and log-linear models. Through this course, they will develop the ability to understand the characteristics of data, choose and apply appropriate analysis methods. Moreover, the course covers the application and implementation of these methods on real data through statistical software.

This course introduces several effective methods to collect and analyze data to improve processes, reduce costs, and understand a complex system for both scientific research and industrial applications. The course covers complete randomized design, Latin square design, factorial design, block design, fractional factorial design, and response surface analysis. Students will also learn how to analyze data collected according to each design method. A prerequisite for this course is ‘Regression Analysis and Lab.’.

Multivariate statistical methods are important tools in applied machine learning and statistics. In this course, you will learn to make inferences about multivariate normal distributions and explore dimensionality reduction methods for high-dimensional data such as principal component analysis and factor analysis, as well as data mining techniques such as classification and clustering methods.
(Prerequistes: Mathematical Statistics 1, Linear Algebra 1, Regression Analysis and Lab.)

Bayesian inference is a statistical inference method that represents information with uncertainty as a probability, updates the information with Bayes theorem and make inference using the posterior distribution. This course covers the historical background of Bayesian inference, the basic elements of Bayesian inference, and the Bayesian computational methods needed for practical Bayesian inference, including Markov chain Monte Carlo and variational methods. This course also deals with how to apply Bayesian inference to basic statistical models such as two-sample tests, linear regression, and logistic regression.

Data mining is the study of discovering hidden patterns in large datasets and making predictions. The application of data mining spans a wide range of domains, such as web page search, recommendation systems, video data analysis, and cancer prediction. This course introduces students to diverse data analysis methodologies and provides them with opportunities to apply these techniques. In this course, students will learn about information retrieval, dimension reduction, penalized regression, model selection and validation, classification, clustering, and ensemble methods. Students will learn to choose appropriate data analysis methods to address specific problems or objectives, thereby enhancing their problem-solving skills.

This course covers time series analysis methods and the use of statistical packages. It includes topics such as the properties of ARIMA models, seasonal ARIMA models, GARCH models, integer-valued time series models, and inferences for these models, including change point analysis.

In this course, students learn statistical methods to assess abnormality from various forms of data observed over time. The course covers univariate/multivariate Shewhart control charts, CUSUM control charts, EWMA control charts, and non-parametric control charts for both numerical or quantitative data. Some courses that may be helpful include <Multivariate Data Analysis and Lab.>, <Mathematical Statistics 1>, <Mathematical Statistics 2>, <Nonparametric Statistics and Lab.>, and <Time Series Analysis and Lab.>

This course covers both discrete and continuous Markov chains, including topics like recurrence, ergodicity, reversibility, absorptions, and their applications, such as MCMC. It also delves into renewal processes, encompassing Poisson processes, various renewal theorems, and their applications.

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.

This course emphasizes advanced analytical skills for effective problem-solving. Moving beyond basic data management, exploratory analysis, and visualization techniques, we delve into specialized analytical approaches suitable for complex data structures and various data types. Students will have the opportunity to develop deep insights and strategies for complex challenges using advanced data science methodologies. Furthermore, through exploring various statistical topics, we gain a comprehensive approach to data analysis.

Statistics provides the theoretical foundation for rational decision-making based on data. This course covers the concepts of probability and random variables, the basis of statistical theory, as well as statistical estimation and testing. Through this course, students will be able to understand the process of data generation as a probability model and will learn the theoretical foundation for statistical inference methods for data analysis.

※ 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.