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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/exploratory-data-analysis
课程评论：没有评论
课程名称：探索性数据分析 概述：本课程涵盖了总结数据的基本探索性技术。这些技术通常在正式建模开始之前应用，并可以帮助指导更复杂统计模型的开发。探索性技术对于消除或明确可以通过数据解决的潜在假设也是至关重要的。我们将详细介绍 R 中的绘图系统以及构建数据图形的一些基本原则。同时，我们还将涵盖一些常见的多变量统计技术，用于可视化高维数据。 课程大纲： - 第1周：本周介绍分析图形的基础知识以及 R 中的基础绘图系统。为帮助尚未安装 R 的学员，我们还提供了一些背景资料。 - 第2周：欢迎来到探索性数据分析的第2周。本周讨论 R 中一些更高级的图形系统，包括 Lattice 系统和 ggplot2 系统。尽管基础图形系统提供了许多可视化数据的重要工具，但它是原始 R 系统的一部分，缺乏一些在绘制高维数据时可能需要的功能。Lattice 和 ggplot2 系统简化了图形的布局，使得绘图过程不再繁琐。 - 第3周：欢迎来到探索性数据分析的第3周。本周涵盖了一些探索性分析的常用统计方法，包括聚类和降维技术，这些方法使您能够对非常高维的数据（大量变量）进行图形显示。我们还将介绍在 R 中指定颜色的新方法，以便在制作数据图形时将颜色作为一个重要且有用的维度使用。所有这些内容在我书中的第9-12章中都有涉及。 - 第4周：本周，我们将查看两个探索性数据分析的案例研究。第一个案例涉及聚类分析技术，第二个案例则是对一些空气污染数据的深入分析。尽管进行探索性数据分析的方式因人而异，我提供这些视频是为了让您对如何处理特定类型的数据集有一个初步的了解。
Name:Week 1
Description:This week covers the basics of analytic graphics and the base plotting system in R. We've also included some background material to help you install R if you haven't done so already.
Name:Week 2
Description:Welcome to Week 2 of Exploratory Data Analysis. This week covers some of the more advanced graphing systems available in R: the Lattice system and the ggplot2 system. While the base graphics system provides many important tools for visualizing data, it was part of the original R system and lacks many features that may be desirable in a plotting system, particularly when visualizing high dimensional data. The Lattice and ggplot2 systems also simplify the laying out of plots making it a much less tedious process.
Name:Week 3
Description:Welcome to Week 3 of Exploratory Data Analysis. This week covers some of the workhorse statistical methods for exploratory analysis. These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dimensional data (many many variables). We also cover novel ways to specify colors in R so that you can use color as an important and useful dimension when making data graphics. All of this material is covered in chapters 9-12 of my book Exploratory Data Analysis with R.
Name:Week 4
Description:This week, we'll look at two case studies in exploratory data analysis. The first involves the use of cluster analysis techniques, and the second is a more involved analysis of some air pollution data. How one goes about doing EDA is often personal, but I'm providing these videos to give you a sense of how you might proceed with a specific type of dataset.
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
