探索性数据分析

Exploratory Data Analysis

Part of the 数据科学 Specialization »
学习分析数据时必要的探索性技巧。这是约翰霍普金斯数据科学专项课程的第四门课。

约翰霍普金斯大学

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探索性数据分析

Learn the essential exploratory techniques for summarizing data. This is the fourth course in the Johns Hopkins Data Science Specialization.

课程简介

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

After successfully completing this course you will be able to make visual representations of data using the base, lattice, and ggplot2 plotting systems in R, apply basic principles of data graphics to create rich analytic graphics from different types of datasets, construct exploratory summaries of data in support of a specific question, and create visualizations of multidimensional data using exploratory multivariate statistical techniques.

背景知识

先学习“数据科学家的常用工具”和“R语言编程”这两套课程。

授课形式

There will be weekly video lectures, quizzes, and peer assessments.

As part of this class you will be required to set up a GitHub account. GitHub is a tool for collaborative code sharing and editing. During this course and other courses in the Specialization you will be submitting links to files you publicly place in your GitHub account as part of peer evaluation. If you are concerned about preserving your anonymity you will need to set up an anonymous GitHub account and be careful not to include any information you do not want made available to peer evaluators.

常见问题

学完这门课,我能得到结课证书吗?
凡顺利完成本课程的学生均可获得由授课老师签发的结课证书。

选修这门课需要准备什么?
学生必须拥有一个激活的Github账户,并安装最新版的R语言和RStudio软件。

这门课与数据科学系列课程之间应该怎样合理安排?
这是该系列课程的第4部,我们强烈建议大家先学习《数据科学家的常用工具》和《R语言编程》这两套课程。

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