Data Science: Wrangling
Learn an indispensable part of data science known as data wrangling, a process that involves converting raw data to formats needed for further analysis.
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Description
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.
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.
Pricing:
Free
Free
Level:
Beginner
Beginner
Duration:
4 weeks, 2h-4h/week
4 weeks, 2h-4h/week
Educator:
Rafael Irizarry
Rafael Irizarry
Organization:
Harvard University
Harvard University
Submitted by:
Coursearena
Coursearena
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