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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-mining-with-rattle/
课程评论：没有评论
课程名称：使用Rattle进行数据挖掘 课程概述：使用Rattle进行数据挖掘是一门独特的课程，既教授数据挖掘的基本概念，又实际使用一款流行的现代数据挖掘软件工具“数据挖掘者”（即“Rattle”包）。Rattle是基于图形用户界面的软件工具，构建在R软件之上。课程重点围绕生命周期问题、过程和与支持“从摇篮到坟墓”的数据挖掘项目相关的任务。这些任务包括：数据探索与可视化；测试数据的随机变量特征和分布假设；按尺度或数据类型转换数据；进行聚类分析；创建、分析和解释关联规则；以及创建和评估可能利用回归、广义线性模型（GLM）、决策树、递归分割、随机森林、提升以及支持向量机（SVM）等范式的预测模型。该课程兼具概念性和实用性，教授数据挖掘知识，并提供大量使用Rattle R包进行数据挖掘任务的演示。课程非常适合希望掌握额外“需求”分析职业技能的本科生，也适合希望学习多种技术以分析研究数据的研究生。此外，该课程还对希望获取和掌握更广泛有用职业技能和知识的定量分析专业人士有帮助。课程内容分为10个独立主题，每个主题都是参与者每周学习的重点。
Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the 'Rattle' package in R software. Rattle is a popular GUI-based software tool which 'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a 'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional 'in-demand' analytical job skills to offer a prospective employer. The course is also suitable for graduate students seeking to learn a variety of techniques useful to analyze research data. Finally, the course is useful for practicing quantitative analysis professionals who seek to acquire and master a wider set of useful job skills and knowledge. The course topics are scheduled in 10 distinct topics, each of which should be the focus of study for a course participant in a separate week per section topic.
