|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/tidyverse-data-wrangling
课程评论：没有评论
课程名称：Tidyverse中的数据整理 课程概述：在数据分析中，数据通常并不是以需要的状态到达的。为了进行有效的数据分析，必须对数据进行重组、重新排列和重新格式化，以便能够进行可视化或者输入到机器学习算法中。本课程解决了数据整理的问题，旨在让你能够有效地控制和分析数据。数据整理的关键目标是将非整洁数据转化为整洁数据。 课程涵盖了在R语言中处理整洁和非整洁数据的一些关键细节，包括从宽格式转为长格式、使用dplyr包操作表格、理解不同的R数据类型、利用正则表达式处理文本数据，以及进行基本的探索性数据分析。掌握这些数据整理技术将使你的分析更加高效、可重复且更加易于理解。 本专业课程假设学员对R编程语言有一定的熟悉度。如果你尚未学习R，建议先完成R编程课程，再再次回到本课程。 课程大纲： 第一部分：Tidyverse中的数据整理 描述：数据通常不是以有效分析所需的状态出现，需要进行重组和格式化。本模块讨论了如何有效整理数据，关键目标是将非整洁数据转化为整洁数据。 第二部分：因子、日期与时间的处理 描述：在R中，分类数据被视为因子。分类数据有一定的可能值。例如，月份变量的每个观察值只能是12个月中之一。有效处理这种变量对于数据分析至关重要。 第三部分：探索性数据分析 描述：探索性分析的目标是检查数据并发现之前未知的关系。探索性分析探讨不同测量之间的关系，但不确认其因果关系。正如“相关性并不意味着因果性”的说法，探索性分析的结果并不一定意味着一种变量引起了另一种变量的变化。 第四部分：项目：在Tidyverse中整理数据 描述：在此项目中，你将使用消费者金融保护局（CFPB）的消费者投诉数据，练习数据探索和数据整理技能。
Part: 1
Title:Wrangling Data in the Tidyverse
Description:Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This module addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data.
Part: 2
Title:Working With Factors, Dates, and Times
Description:In R, categorical data are handled as factors. By definition, categorical data are limited in that they have a set number of possible values they can take. For example, there are 12 months in a calendar year. In a month variable, each observation is limited to taking one of these twelve values. Thus, with a limited number of possible values, month is a categorical variable. Categorical data, which will be referred to as factors for the rest of this lesson, are regularly found in data. Learning how to work with this type of variable effectively will be incredibly helpful.
Part: 3
Title:Exploratory Data Analysis
Description:The goal of an exploratory analysis is to examine, or explore the data and find relationships that weren’t previously known. Exploratory analyses explore how different measures might be related to each other but do not confirm that relationship as causal, i.e., one variable causing another. You’ve probably heard the phrase “Correlation does not imply causation,” and exploratory analyses lie at the root of this saying. Just because you observe a relationship between two variables during exploratory analysis, it does not mean that one necessarily causes the other.
Part: 4
Title:Project: Wrangling data in the Tidyverse
Description:In this project, you will practice data exploration and data wrangling with the tidyverse using consumer complaint data from the Consumer Financial Protection Bureau (CFPB).
Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data. This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Investing the time to learn these data wrangling techniques will make your analyses more efficient, more reproducible, and more understandable to your data science team. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
