Department of Statistics

Introduction

Statistics is not only a set of tools used for organizing data but also the science of analyzing various kinds of natural, social and economic phenomena based on information contained in the data.

Statistics is an expansive discipline that encompasses areas ranging from the development of methodology for data collection and the analysis, to decision-making in many challenging problems under uncertainty. In Particular, the intensive statistical procedures are able to be applied to the complicated statistical problems which arise in diverse fields since the modern computing technology has been developed both rapidly and innovatively. We provide a quality education of Statistics including quantitative abilities, statistical knowledge, and communication skills to work on many challenging problems in the real world.

History

The Department of Statistics was founded in the College of Natural Sciences in 1981. At present, the Bachelor of Statistics consists of 7 permanent faculty members and provides Master and PhD programs. The department offers training programs balancing both the theory and the application of Statistics.

Job Fields

Recently, there has been an increasing demand for statisticians in a government, industrial laboratories, and private companies (bank, marketing and insurance companies, computer programming companies, etc.). This reflects the fact that being statistician is one of the top promising professional occupations in the 21st century.

Faculty

Minjung Kwak, Professor

- University of Wisconsin - Madison
mjkwak@yu.ac.kr
Lee, Jiyeon, Professor

- POSTECH , Applied Probability
leejy@yu.ac.kr
Kim Byungsoo, Associate Professor

- Seoul National Univ.
bkim@yu.ac.kr
Kyungsub Lee, Associate Professor

- KAIST
ksublee@yu.ac.kr

※ sorted by the position and Korean name

Curriculum
1-1	CONVERGENCE- AND INTEGRATION-BASED THINKING AND WRITING The ability of analyzing and solving problems is emphasized as one of the conditions for survival in the 21st century and one of the core elements of creative capabilities. This course was designed to cultivate convergence- and integration-based creative capabilities, which are integrated problem-solving capabilities to collect, analyze and process knowledge and information by reinforcing the ability of analyzing and solving problems, recreate it in a synthetic fashion, and express it effectively through speech and writing. The course will help the students cultivate their synesthesia thinking and communication skills based on sympathy with other human beings, understanding of the community, and positivity or Gongseong that is sought after by Yeungnam University. Its ultimate goals are to promote the students' creative knowledge development and reinforce their writing capabilities consistently through "convergence- and integration-based thinking and writing as a problem-solving approach."
1-1	SEMINAR FOR ACADEMIC LIFE 1. Summary of the course This course is to assist university freshmen in CRM designing to adapt university life well through the instruction and counselling of supervising professor. (This course is composed of self analysis, personality type test, career research, instruction for the success of university life, career plan and direction setting, CRM designing method and CRM designing. The course should be teaching in classes of the students by supervising professor.) 2. Course objectives This course is to motivate the students before the mid term exam and provide students with self analysis, personality type test (MBTI or TCI) and career research (YAT test). Also, this course shall has a plan to instruct the students to enhance the efficiency of university life through career and time management. In addition, this course is to make a chance for the students to have practical assistance to university life by providing study method, report designing strategy and the information on academic system and various kinds of internal programs of the university. After the mid term exam, the students will be instructed to set the direction of career designing through continuous counselling of supervising professor and the students will be able to establish CRM designing and execution plan.
1-1	SOCIAL CONTRIBUTION AND SERVICE This course is to cultivate community sense as members of society and the global village for students in order to develop the basic knowledge required as global citizens. Especially, this course is to foster the spirit of cooperation, sharing, service, and creativity and study the social contribution and leadership to solving the challenges the global community faces. As a liberal arts course, it is centered to nurture a leader having the global capability to contribute to community development through learning the knowledge and the case on the value & logic of social responsibility focused on environmental preservation, social contribution, and good governance(ESG). This course aims to foster a generous mind, learn knowledge and technology and build the capacity to contribute to building a society towards a safer and happier world through the study of theory and practice.
1-1	SOFTWARE AND AI Software and AI (Artificial Intelligence) course aims to educate the basic concepts of software and computational thinking to use them in various applications. It allows students of various majors to experience the core technologies of the 4th industrial revolution, such as big data, machine learning, and AI. It also introduces various applications of AI so that students can easily apply these technologies to their field of study. This course classifies the lecture types into three categories, and adjust the lecture difficulty according to the student's academic ability.
1-1	STATISTICS(1) This course defines events and probabilities, conditional probabilities and independence to evaluate probabilities. Elementary probability distributions such as binomial distribution, geometric distribution, Poisson distribution, and normal distribution are also introduced. The concepts of sample distribution of the statistic and the central limit theorem are introduced. The statistical inference including estimation and hypothesis testing of the mean and the standard deviation will be discussed.
1-2	APPLIED STATISTICS It is a lecture on statistical methods which abstract and summarize information from raw data. Descriptive statistics of which histogram, scatter plot, etc. are main issues and concepts of probability which is the basic paradigm for sample behavior, will be treated. Important concepts in statistical inference will be also covered. Based on these fundamental concepts, testing of the mean, variance and proportion, comparison of two means, correlation analysis, analysis of variance, regression analysis, categorical data analysis, time series analysis, non-parametric methods will be taught. Also students will have laboratories for real data analysis with statistical packages.
1-2	PRACTICAL ENGLISH The aim of the course is to help students develop basic English verbal skills in real life situations. The course will be co-taught by Korean and Native English instructors. Korean instructors will provide students with basic English structure, vocabulary, and expressions, and students will be encouraged to practice speaking English utilizing basic English structures. Students will further practice expressing themselves in English with native English instructors.
1-2	STATISTICS(2) Based on the elementary theory of Statistics (1), statistical data analysis methods using estimation and hypothesis testing will be introduced. Main topics in statistical inference such as point estimation, confidence intervals, hypothesis testing, test statistics, rejection region, significance level, and p-value will be covered. With understanding of these fundamental concepts, testing of the mean, variance, and proportion, comparison of two means, experimental design, analysis of variance, multiple comparisons, regression analysis, correlation analysis, categorical data analysis, nonparametric method will be taught.

Curriculum
2-1	LOGIC AND ESSAY WRITING IN MATHEMATICAL EDUCATION This course disciplines Logic and Essay writing in Mathematical Education
2-1	MATHEMATICS FOR STATISTICS It deals with calculus including continuity and limit of functions, differentiations and integrations, sequences and series and matrix algebra covering determinants of matrix, inverse matrix, eigenvalues and eigenvectors which are necessary to understand theoretical and computational derivation of probability and statistics.
2-1	PROGRAMMING FOR STATISTICS This course uses Python to study the basic methods of programming. Students will learn about the basic logic of programming through variables, functions, conditional statements, and iterative statements. Through programming using additional Python modules for visualization, scientific calculation, and data processing, we broaden our understanding of the statistics-related theories. If time permits, the concepts of classes and objects will be taught.
2-1	STATISTICAL SURVEY AND ANALYSIS Design of statistical surveys, and basic concept of statistical survey and analysis including biases, variances and cost estimators, comparison of simple random sampling, stratified sampling, systematic sampling, balanced systematic sampling, stratified systematic sampling, simple cluster sample, unequal probability sampling, multistage cluster sampling, ratio estimation.
2-1	ELEMENTARY PROBABILITY & DISTRIBUTION THEORY It introduces the probability distribution, which consists of theoretical backgrounds of statistics such as basic concept of probability, discrete and continuous random variables, the expectation, moment generating functions and Chebychev's theorem.
2-1	STATISTICAL PACKAGE In this course basic concepts and structures of SAS are dealt with. Commands for reading and writing of data and merging and deviding of data set are studied.
2-2	ENGLISH FOR STATISTICS It is English essays reading course. Articles and essays in statistical history, statistical methods, an statistical episodes will be read.
2-2	STATISTICAL COMPUTATION It is essential to use computer programs in statistical data analysis, The aim of this course is to help students understand the computer language C. Programming projects for pdf graphs, hypothesis testing statistics will be worked out in classes.
2-2	STATISTICAL DATABASE Various ways to utilize the SQL language used in modern database systems will be taught. SQL is the most widely used query language to access databases, manipulate, insert, modify and delete data. Students will learn about SQL's basic and advanced methods and relational database system concepts. Further topics include how to use SQL by connecting to a database through other programming languages.
2-2	STATISTICAL METHOD Summarizing data, the distribution of the sample mean and the estimation of the population mean in large samples, Type-I and Type-II errors, hypothesis testing for the population mean and proportion in large samples, small-sample inference for the mean and variance in normal populations, two-sample comparisons, and categorical data are discussed.
2-2	STATISTICAL QUALITY CONTROL This is principal applied statistical course for high quality management. We discuss about Methods of Quality Modeling process Quality, Inferences About process Quality, Statistical Process Control, Control Charts, and EWMA chart etc.

Curriculum
3-1	ACTUARIAL STATISTICS In this course the followings will be taught: Development of new products and the setting of premium rates for all types of policies. Investigations into the financial soundness of the life assurer, and the determination of bonus rates on policies. Statistical investigations into mortality rates, disability rates, expenses, lapse and surrender rates. The setting of the terms under which life assurance policies can be altered or discontinued. Life reassurance reassurance premiums, development of new products, and reserving methods.
3-1	INTRODUCTION TO MATHEMATICAL STATISTICS I Distribution of random variables, density function, moment generating functions, some special distributions, distribution of functions of random variables, distribution of order statistics, limiting distributions.
3-1	REGRESSION ANALYSIS Regression analysis is often used tools in the statistician's toolbox. The theory is elegant and the computational problems intriguing. so that both "pure" and "applied" statisticians can feel at home in the subject. For example, among the theoreticians there is still an ongoing interest in least squares with all its generalizations and special cases. At the same time practitioners continue to develop a wide range of graphic methods for testing models and examining underlying assumptions. As numerical analysis and statistics have slowly interwined, statisticians have been made to realize the difficulties associated with certain time-honored computational procedures. The development of regression computer programs that are efficient and accurate is now recognized as an important part of statistical research.
3-1	STOCHASTIC SIMULATIONS In this lecture, we introduce stochastic simulation methods with R. Stochastic simulation is the best way to develop statistical intuitions and check theories. The programming language R supports remarkable statistical implementations and students will learn how to turn algorithms into code. Topics include R basic programming; input and output; functions; sophisticated data structures; visualization; numerical integration; probability; random variable; estimation; simulation.
3-2	BIG DATA ANALYTICS Big data needs particular types of methodologies and techniques to store, organize and process beyond the approaches in traditional data handling. In this course, we study what big data is, where big data comes from and suitable methods for big data systems and programming. In addition, big data modeling, management systems, big data integration and processing will be taught. Topics to be covered: the basic concept of file distributed storage, Mapreduce/Hadoop system, NoSQL stores and languages for big data manipulation and process, statistical package for big data analysis and visualization for big data. Also machine learning with big data, if time permits.
3-2	PEDAGOGY FOR TEACHING SECONDARY SCHOOL MATHEMATICS This course develops various teaching methods and instructional strategies of secondary school mathematics for effective teaching on the basis of contemporary learning psychology. This course also provide teaching practicum for utilizing such pedagogical knowledge in mathematics.
3-2	STATISTICS CAPSTONE DESIGN I This course is oriented to develop and increase the research capacity of the juniors or seniors in Statistics major. In this course students are subjected to design and carry out a research project, by applying some basic theories and experimental methods. An intimate relationship between the students and the advising professor will be held on analyzing experimental data performing further experiments.
3-2	STATISTICS FIELD WORK I The objective of this course is designed for the students in the major of Statistics to achieve both the practical knowledge and the experience in the related job fields.
3-2	STOCHASTIC PROCESS MODELING In stochastic processes, Markov chain, Poisson process, Birth and Death Process, Queueing, Network, Inventory Process will be introduced. In PC Window environment, these models will be implemented with C++. Properties of the models will be investigated using programs implemented in classes.
3-2	DESIGN AND ANALYSIS OF EXPERIMENTS It is applied to various fields such as applied science. cultural and social science which require experimentation or surveys. It is concerned with the experimental designs, order, methods and analysis. topics for this subject are completely randomized designs, blocking designs, factorial designs and plots design. It requires a level of one semester elementary statistics. Using of statistical computer package such as SAS/STAT,SPSS requires a little prior familiarity with computing.
3-2	INTRODUCTION TO MATHEMATICAL STATISTICS II Estimation, sufficiency statistic, Cramer-Rao lower bound, Bayes estimation, confidence intervals, hypotheses testing, uniformly most powerful tests, likelihoods ratio test, chi-square tests, non-parametric tests.
3-2	MULTIVARIATE DATA ANALYSIS This is an introductory course of multivariate data analysis as applied economics, engineering, the natural and the social sciences. This one has as prerequisites only a knowledge of elementary statistics, Use of statistical computer package such as SAS/ETS, SPSS, SPLUS requires a little prior familiarity with computing. Topics in this course are Principal Component Analysis, Factor Analysis, Discriminant Analysis and Cluster Analysis.

Curriculum
4-1	INTRODUCTION TO BAYESIAN STATISTICS It introduce the basic principles of Bayesian statistics and practical Bayesian methodology. Probability and uncertainty, Baye's theorem, prior distribution, posterior distribution, conjugate prior distribution, Bayesian inference, and Bayesian decision theory are discussed.
4-1	INTRODUCTION TO DATAMINING The explosive growth in stored data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amount of data into useful information and knowledge. This is data mining and has recently become very popular in business with customer relationship management(CRM). The main tool is related to Multivariate Analysis in Statistics. This course includes laboratory with Clementine of SPSS or E-miner of SAS.
4-1	INTRODUCTION TO FINANCIAL STATISTICS This course introduces the modern portfolio and utility theories based on probabilistic methods. Value-at-Risk and its computation method via computer programs are studied where the GARCH and FHS methods will be applied. Also, students will learn about statistical methods for backtesting.
4-1	STATISTICS CAPSTONE DESIGN Ⅱ This course is oriented to develop and increase the research capacity of the juniors or seniors in Statistics major. In this course students are subjected to design and carry out a research project, by applying some basic theories and experimental methods. An intimate relationship between the students and the advising professor will be held on analyzing experimental data performing further experiments.
4-1	STATISTICS FIELD WORK Ⅱ The objective of this course is designed for the students in the major of Statistics to achieve both the practical knowledge and the experience in the related job fields.
4-1	THEORY OF TEACHING SCHOOL MATHEMATICS This course provides the foundational knowledge of mathematics education by studying the nature and historical development of mathematics, psychology of learning mathematics, various instructional strategics, assessment and evaluation, techniques, and technology for school mathematics for the prospective teachers.
4-1	TIME SERIES ANALYSIS AND FORECASTING This is an introductory course of time series analysis and forecasting methods as applied in economics, engineering and the natural and the social sciences. This one has as prerequisites only a knowledge of elementary statistics. Use of statistical computer package such as SAS/ETS, SPSS, SPLUS requires a little prior familiarity with computing. Topics in this course are trend and seasonality, stationary processes, model identification method, modelling and forecasting with ARMA models, nonstationary and seasonal models and forecasting.
4-2	CATEGORICAL DATA ANALYSIS It is known as Logistic Analysis for general biological data analysis. It includes Logistic(multiple) Regression Model, Coefficients of Logistic Model Building strategies, and measures of Goodness-of-Fit.
4-2	FINANCIAL STATISTICS In this course, we study the probabilistic methods for derivative pricing. Based on the principle of no-arbitrage, we will examine the theory and practice of option pricing under the binomial tree model and geometric Brownian motion model. Furthermore, interest rate hedging strategy, risk-neutral probability measure, implied volatility, and VIX will be taught.
4-2	INTRODUCTION TO NONPARAMETRIC STATISTICS Technique and application of nonparametric tests, One sample tests, Two sample tests, one-way layout, Two-way layout, Correlation, Regression.
4-2	STATISTICAL DATA ANALYSIS Using actual data obtained through investigations in various fields, various statistical analysis are performed according to procedures such as a statistical problem setting, selection of an appropriate analysis method, statistical analysis with a statistical package, and correct interpretation of results. By conducting analysis based on case studies in various fields, students can develop practical problem-solving skills.
4-2	STATISTICAL MACHINE LEARNING Statistical machine learning is the process of understanding complex data sets and performing inferences and predictions through various statistical modeling. In this course, the theory and applications of various machine learning are studied, including regression, classification, tree, boosting, support vector machines, neural networks and so on. In the era of big data, statistical machine learning is used in various fields such as medicine, finance, management, and marketing as well as traditional scientific fields.

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+82-53-810-2320
+82-53-810-4615

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