Statistics + Follow Followed

经济统计学专业是统计学在经济领域中的应用学科，是以经济数据为研究对象，包括经济数据的采集、生成和传输，用统计方法分析经济数据背后的经济现象以及复杂经济系统的规律，从而为经济和管理决策服务。

科学技术统计学是通过统计指标体系来分析、研究科学技术经济数量关系的新兴学科。科学技术统计学以科学技术与经济、社会发展有数量关系的问题为研究对象，探讨科学技术对经济、社会的影响以及推广新技术、新工艺、新产品所取得的经济效果问题。科学技术统计学是20世纪以来，随着世界科学技术的发展而逐步形成的一门应用性学科。

应用统计学主要从应用的角度阐述统计数据或统计信息获取、处理、推断、分析和应用的一系列统计理论和统计方法。 统计学可以简单地分为两大类：一类是以抽象的数量为研究现象，研究一般的控集数据、分析数据方法的理论统计学；另一类是以各个不同领域的具体数量为研究对象的应用统计学。因此，应用统计学的研究对象是：现象总体的数量方面，即现象总体的数量特征和数量关系。

社会统计学（social statistics）是统计学的应用分支，有系统地搜集、整理、分析、呈现社会环境中人类行为的数据资料，显现资料的性质，帮助个人、团体、企业或政府推论未来情况，并做出适当的决策。研究者利用随机抽样方法取得母体或样本资料后，可以发现资料的规律性，借此进行经验探究。由于社会统计学的研究对象是人类社会，常以个人为分析单位；因此，在测量尺度上相当重视名义尺度与顺序尺度的资料，而不仅止于等距尺度资料。此外，“人类”有主观意识，容易影响问卷填答与回收，甚至是分析；信度与效度对社会统计学而言是至为重要的问题。

人口统计学用统计学方法，分析人口及其他社会问题，用以解决诸如预期寿命、出生率、死亡率等资料，以推估未来人口移动以及变化等问题 。它通过数量表现揭示人口现象的本质、规律和发展趋势，是人口学的重要组成部分， 人口统计学作为方法论学科，也是社会经济统计学的重要组成部分。

生物统计学（有时也称生物计量学）是统计学的原理和方法在生物学研究中的应用，是一门应用数学，最常见的是应用于医学。 医学统计学 （有时以生物测定学著名， 虽然生物测定学的新发展是涉及另外一个领域）一般来说是统计对生物学和医学的应用。由于在生物和医学研究问题是各种各样的， 生物统计学扩展它的领域包括任何定量，不仅统计，也许其他模型。对临床试验的设计和分析就是医学统计学在医学里最明显的应用。

商业分析包括审视预计的销售额、成本和利润是否达到公司预计目标；如达到，则此产品概念才能进一步发展到产品开发阶段。

金融统计是指金融机构统计部门对各项金融业务活动的情况和资料进行收集、整理和分析的活动。

机器学习和数据挖掘是近20多年兴起的一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、计算复杂性理论等多门学科。机器学习理论主要是设计和分析一些让计算机可以自动“学习”的算法。机器学习算法是一类从数据中自动分析获得规律，并利用规律对未知数据进行预测的算法。因为学习算法中涉及了大量的统计学理论，机器学习与推断统计学联系尤为密切，也被称为统计学习理论。算法设计方面，机器学习理论关注可以实现的，行之有效的学习算法。很多推论问题属于无程序可循难度，所以部分的机器学习研究是开发容易处理的近似算法。 机器学习已广泛应用于数据挖掘、计算机视觉、自然语言处理、生物特征识别、搜索引擎、医学诊断、检测信用卡欺诈、证券市场分析、DNA序列测序、语音和手写识别、战略游戏和机器人等领域。

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.

Ordinary Differential Equations is a basic course for mathematical students. In this course, the students will learn the basic knowledge of ordinary differential equations, including how to solve some simple equations, the existence and uniqueness for Cauchy problem, boundary value problems as well as the theory of linear differential equations.

The course teaches student using the SAS system with an easy starting attitude, it includes SAS programming, data management, reporting and graphics, basic statistical analysis techniques. The course will also introduce R, another statistical software. R is especially suitable for programming statistical algorithms, and it is one of the most prefered developement and computing tools used by statisticians.

This Course aims at guiding students to describing and modelling non-determinative iphenomenons mathematically, and provides chances for students to practice Set Theory,Calculus and Advanced Algebra.

Higher algebra is an important basic course in mathematics and applied mathematics, mathematics and applied mathematics. Its main contents include two parts: polynomial theory and linear algebra. The purpose of this course is to make students master the basic knowledge and system algebra and abstract strict algebraic method for subsequent courses such as algebra, differential equations, probability theory and mathematical statistics, functional analysis, calculation methods to provide necessary algebraic knowledge, but also provide a training for further study all the courses of mathematics and Applied Mathematics needed for abstract thinking ability. Higher algebra is to continue and improve the high school algebra. Through the course of teaching, to enable students to deepen the understanding of high school algebra.

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.