《多元应用统计分析》课程简介 （72课时）

随着计算机技术的发展，各个领域对数据分析与计算的需求也越来越高，如何从数据中得到有效的信息和知识成为关键问题。本课程主要讲授数据挖掘的基本理论与概念、多元统计的各个分支及应用。包括DM的定义、流程、数据预处理、描述性统计分析、数据可视化、关联分析、聚类、分类、异常点分析等几个主要的部分，其中数据预处理包括数据的标准化、空缺值处理、噪音数据处理、数据规约；数据规约部分介绍数据规约的方法，包括主成分分析和因子分析；关联分析主要讲授关联规则、Aproiri算法、相关分析；聚类部分主要讲授K-means和层次聚类分析；分类部分主要讲授判别分析、决策树、MCLP、多元回归等算法与应用。

课程通过案例分析、上机练习提高学生应用数据挖掘和多元统计的方法解决实际问题的动手能力。开设的实验项目包括数据挖掘技术的实现与应用：数据的预处理、分类，预测，关联和聚类；以及设计到的多元应用统计方法的实现：方差分析、相关分析、主成分分析、因子分析等。所需软件：Excel、SPSS、SEE5等

Applied Multivariate Statistical Analysis ( 72 hours )

With the development of computer technology, the demand for data analysis and calculations in various fields are also increasing. How to get effective information and knowledge from the data has become the key issues. The basic theories and concepts of data mining (DM), the various branches of multivariate statistics would be taught in this course. It includes the definition and processes of DM, data preprocessing, descriptive statistical analysis, data visualization, association analysis, clustering, classification, and outlier analysis and so on. Data preprocessing includes data standardized, data cleaning, data transformation, data integrated, and data reduction. Principal component analysis (PCA) and factor analysis would be introduced in this part. Association analysis includes association rules, Aproiri algorithm, and correlation analysis. Clustering includes K-means and hierarchical clustering analysis. Classification includes discriminant analysis, decision tree, multi-criteria linear programming, multiple regression algorithms and applications.

Case studies and computer exercises are needed in this course for students' practical ability improving. The project includes the application of data mining techniques: data preprocessing, classification, prediction, association, clustering, factor analysis, correlation analysis, principal component analysis and so on. Software required: Excel, SPSS, SEE5 etc.

《多元应用统计分析》课程教学进度计划