Data Science: Wrangling
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.
Created by: Harvard University
Level: Introductory
Created by: Harvard University
Level: Introductory
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Darío, trabajador en una ONG en Colombia y participante del curso, apunta que aplicó los conocimientos del curso para crear DATASIMUS, un portal de datos de movilidad urbana que compara más de 400 variables de sistemas de transporte de la región. Descubre más abajo otros testimonios sobre el impa...
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