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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-cleaning-in-r-tidyverse/
课程评论：没有评论
课程名称：使用Tidyverse和Data.table进行R语言数据清洗 课程概述：欢迎参加本课程，专注于使用R语言中的Tidyverse、Dplyr、Data.table、Tidyr等多个包进行数据清洗。您可能已经遇到过这样的问题：在分析之前，数据未得到适当清洗，导致结果失真，甚至无法进行分析。简而言之，数据科学的首要步骤就是数据清洗。无论您使用何种数据或进行何种分析，数据清洗都是必不可少的。因此，投资时间学习这个过程非常明智。 课程将帮助您认识到原始数据中可能出现的各种问题，以及如何使用多种工具和函数来应对这些问题。R语言为每种情境都准备了解决方案，从异常值检测、缺失数据填补到列的拆分与合并、字符操作、类别转换等等，你都可以在R中找到相应的工具。而且，清洗数据的方法有很多种，您总能找到适合自己的方式，无论是简单工具还是复杂的机器学习算法。 课程中，您将学习哪些R工具最为高效，以及如何有效使用这些工具。我们将深入探讨Tidyverse包系统，它是一个协作的包集合，能够在数据清洗的整个流程中为您提供支持，从数据导入到数据查询。您将使用数据处理工具如data.table、tibble和dplyr来过滤和查询数据集。 课程内容还包括如何识别异常值和填补缺失数据，甚至使用机器学习算法来完成这些任务。为确保您能在实际工作中应用所学工具，课程末尾将包含一个数据清洗项目，您将根据所学材料完成一个独立的作业，充分测试和锻炼您的数据清洗技能。 完成课程后，您将获得Udemy颁发的结业证书，并能获取R脚本以便复制到您的RStudio实例中。 R-Tutorials团队希望您在课程中收获丰富的知识与实践经验！
Welcome to this course on Data Cleaning in R with Tidyverse, Dplyr, Data.table, Tidyr and many more packages!You may already know this problem: Your data is not properly cleaned before the analysis so the results are corrupted or you can not even perform the analysis.To be brief: you can not escape the initial cleaning part of data science. No matter which data you use or which analysis you want to perform, data cleaning will be a part of the process. Therefore it is a wise decision to invest your time to properly learn how to do this. Now as you can imagine, there are many things that can go wrong in raw data. Therefore a wide array of tools and functions is required to tackle all these issues. As always in data science, R has a solution ready for any scenario that might arise. Outlier detection, missing data imputation, column splits and unions, character manipulations, class conversions and much more - all of this is available in R. And on top of that there are several ways in how you can do all of these things. That means you always have an alternative if you prefer that one. No matter if you like simple tools or complex machine learning algorithms to clean your data, R has it.Now we do understand that it is overwhelming to identify the right R tools and to use them effectively when you just start out. But that is where we will help you. In this course you will see which R tools are the most efficient ones and how you can use them. You will learn about the tidyverse package system - a collection of packages which works together as a team to produce clean data. This system helps you in the whole data cleaning process starting from data import right until the data query process. It is a very popular toolbox which is absolutely worth it. To filter and query datasets you will use tools like data.table, tibble and dplyr.You will learn how to identify outliers and how to replace missing data. We even use machine learning algorithms to do these things. And to make sure that you can use and implement these tools in your daily work there is a data cleaning project at the end of the course. In this project you get an assignment which you can solve on your own, based on the material you learned in the course. So you have plenty of opportunity to test, train and refine your data cleaning skills.As always you get the R scripts as text to copy into your RStudio instance. And on course completion you will get a course certificate from Udemy.R-Tutorials Team
