The course consists of nine chapters divided into three parts. The first part contains three chapters that provide some preliminary results on matrix theory and multivariate normal and related distributions. Various linear models are also introduced through real examples in this part. The second part consists of two chapters on statistical inference of linear models, including parameter estimation, hypotheses testing, confidence intervals, and prediction. In the third part, the methodologies are applied to various linear models, such as the linear regression model, the analysis of variance model, the analysis of covariance model, the variance components model, and the mixed effect model. The course will emphasize the statistical and geometric motivation for the methods, the practical application of the methods, the implementation by Statistical software, and the interpretation of the results.

This course focuses on standard nonparametric procedures useful for the analysis of experimental data. One-sample, two-sample, and multiple sample rank test and their power are covered. Goodness-of-fit tests, contingency table test are also covered. It also includes some modorn nonparametric techniques such as nonparametric distribution estimation, nonparametric regression, functional data analysises. Theories are are emphasized, such as U-statistics, power function, and asymptotic relative efficiency are introduced, but the applications are not completely neglected, some applications such as gene set enrichment analysis are also included.

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Bioinformatics aims to develop methods and software for processing large amount of biological data, and it has become an important tool in many areas of biology. This course covers major topics of bioinformatics, which includes: Introduction to bioinformatics; Collection and storage of sequences; Sequence alignment; Sequence databases; Introduction to statistics and probability analysis; Recognition of sequence patterns; Molecular evolution and phylogenetic analysis; Genome analysis and gene prediction; RNA bioinformatics; (10) Protein structure analysis and prediction; (11) Microarray data analysis; (12) Biomolecular network. This course also provides hands-on practice on some software and databases of bioinformatics.

Epidemiology is the study and analysis of the patterns, causes, and effects of health and disease conditions in defined populations.

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A science dealing with the collection,analysis,interpretation,and presentation of numerical data

Biostatistics (or biometry) is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results.

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Mathematical analysis is one of the most important courses for the students who wish to study the mathematics and related subjects. The course mainly includes the theory of Riemann integrals and the theory of series. The course is a basis for Mathematical analysis and for many courses such as differential equations; differential geometry, functions of one complex variable; real analysis, probability; basic physics, etc. The course provides the training for the mathematical thinking and skills.

Mathematical Statitics is a basic course with wide application, it mainly focuses on the analysis of randon sample and other data set, including how to effectively collect data, parameter estimation , hypothesis testing, linear model and statistical design. The purpose is to let the students to understand elementary ststistica concepts and ideas, to study the most commonly used statistical methods and to solve some practical problems, and to establish the way of statistical thinking.

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Understand principle and theory of Data Structures and Algorithms, able to design and implement fundamental data structures and algorithms. Covers programming, data structures, algorithms. Topics include the organization and implementation of fundamental data structures such as list, binary tree, tree and forest, and graph; sorting and searching; data abstraction and problem solving.

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Basic probability, Statistical inference,Estimation ,Testing ,Regression

The content of the course consists of polynomials, linear spaces and linear transformations. This course will train the students with mathematical thinkings and the preliminary ability for solving practical problems.