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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-multivariate-analysis-with-r/
课程评论：没有评论
课程名称：应用多变量分析与R 课程概述： 《应用多变量分析与R》是一门实践性强、概念清晰的“动手实践”课程，旨在教导学生如何使用真实数据集和R软件执行多种具体的多变量分析（MVA）任务。该课程是数据挖掘或预测分析、统计或定量建模（包含线性建模、广义线性模型（GLM）、非线性建模、基于协方差的结构方程模型（SEM）规格和估计，以及基于方差的PLS路径模型规格和估计）领域中，任何从业者的极佳背景课程。 学生们将学习多变量数据和多变量分析的基本概念，具体包括如何创建和估计：协方差和相关矩阵；主成分分析（PCA）；多维尺度分析（MDS）；聚类分析；探索性因子分析（EFA）；以及结构方程模型（SEM）的估计。课程还教授如何使用R软件创建多种华丽的2D和3D多变量数据可视化。 所有的R软件、脚本、数据集和课程讲义均在课程材料中提供。课程结构分为七个部分，每部分专注于一个特定的多变量分析主题，且每部分均以“动手实践”的练习结束，以巩固学生对所学MVA概念和技能的理解。该课程是获取“真实世界”预测分析技能的绝佳途径，这些技能在职场中非常抢手。此外，该课程还为研究生和教师提供了分析和解释研究数据所需的相关技能和知识。
Applied Multivariate Analysis (MVA) with R is a practical, conceptual and applied "hands-on" course that teaches students how to perform various specific MVA tasks using real data sets and R software. It is an excellent and practical background course for anyone engaged with educational or professional tasks and responsibilities in the fields of data mining or predictive analytics, statistical or quantitative modeling (including linear, GLM and/or non-linear modeling, covariance-based Structural Equation Modeling (SEM) specification and estimation, and/or variance-based PLS Path Model specification and estimation. Students learn all about the nature of multivariate data and multivariate analysis. Students specifically learn how to create and estimate: covariance and correlation matrices; Principal Components Analyses (PCA); Multidimensional Scaling (MDS); Cluster Analysis; Exploratory Factor Analyses (EFA); and SEM model estimation. The course also teaches how to create dozens of different dazzling 2D and 3D multivariate data visualizations using R software. All software, R scripts, datasets and slides used in all lectures are provided in the course materials. The course is structured as a series of seven sections, each addressing a specific MVA topic and each section culminating with one or more "hands-on" exercises for the students to complete before proceeding to reinforce learning the presented MVA concepts and skills. The course is an excellent vehicle to acquire "real-world" predictive analytics skills that are in high demand today in the workplace. The course is also a fertile source of relevant skills and knowledge for graduate students and faculty who are required to analyze and interpret research data.
