探索性数据分析

Exploratory Data Analysis

1982 次查看
约翰霍普金斯大学
Coursera
  • 完成时间大约为 15 个小时
  • 混合难度
  • 英语, 韩语, 其他, 中文
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Apply cluster analysis techniques to locate patterns in data

Make graphical displays of very high dimensional data

Understand analytic graphics and the base plotting system in R

Use advanced graphing systems such as the Lattice system

课程概况

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.

课程大纲

周1
完成时间为 20 小时
Week 1
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.
15 个视频 (总计 109 分钟), 6 个阅读材料, 7 个测验

周2
完成时间为 17 小时
Week 2
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.
7 个视频 (总计 61 分钟), 1 个阅读材料, 6 个测验

周3
完成时间为 13 小时
Week 3
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.
12 个视频 (总计 77 分钟), 1 个阅读材料, 4 个测验

周4
完成时间为 6 小时
Week 4
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.

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