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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-mining-with-r-go-from-beginner-to-advanced/
课程评论：没有评论
**课程名称：** 使用R进行数据挖掘：从入门到精通！ (Data Mining with R: From Beginner to Advanced!) **课程概述：** 本课程是一门“实践型”的商业分析或数据分析课程，旨在教授如何使用流行的、免费的R软件，通过真实数据和数据挖掘案例，执行数十种数据挖掘任务。课程将教授关键的数据分析、数据挖掘和预测分析技能，包括数据探索、数据可视化以及使用当今工业界和政府部门广泛使用的商业分析软件套件之一R进行数据挖掘。 课程内容围绕数十个数据挖掘任务的演示展开，涵盖分类和预测数据挖掘任务，具体包括：构建分类树、构建和训练决策树、使用随机森林、线性建模、回归、广义线性模型、逻辑回归以及多种聚类分析技术。 此外，课程还将重点介绍和指导“最佳实践”，教授并演示如何安装R软件和RStudio；讲解R中基本数据类型和结构特征；以及如何从键盘输入、用户提示或导入计算机硬盘上的文件将数据输入R会话。所有在数十个案例演示视频课程中使用的软件、幻灯片、数据和R脚本均包含在课程材料中，供学生“带回家”应用于自己的数据分析和挖掘案例。课程还设有各个章节的“动手实践”练习，以巩固学习过程。 **目标受众：** * 寻求获得可就业数据分析技能的本科生和研究生。 * 希望扩展其数据分析和数据挖掘知识与能力储备的预测分析从业者。
This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases. There are also "hands-on" exercises to perform in each course section to reinforce the learning process. The target audience for the course includes undergraduate and graduate students seeking to acquire employable data analytics skills, as well as practicing predictive analytics professionals seeking to expand their repertoire of data analysis and data mining knowledge and capabilities.
