数据科学:数据整理

Data Science: Wrangling

Learn to process and convert raw data into formats needed for analysis.

938 次查看
哈佛大学
edX
  • 完成时间大约为 8
  • 初级
  • 英语
注: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

Importing data into R fromdifferent file formats

Web scraping

How to tidy data using the tidyverse tobetter facilitateanalysis

String processing with regular expressions (regex)

Wrangling data using dplyr

How to workwith dates and times as file formats

Text mining

课程概况

In this course, part of our Professional Certificate Program in Data Science,we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data scientist will likely face them all at some point.

Very rarely is data easily accessible in a data science project. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data into R and tidy the data, using the tidyverse package. The steps that convert data from its raw form to the tidy form is called data wrangling.

This process is a critical step for any data scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.

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