Very rarely in a data science project is data easily available as part of a package. It's more typical for the data to be in a file, a database, or extracted from a document 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. This usually involves several, often complicated, steps to convert data from its raw form to the tidy form that greatly facilitates the rest of the analysis. We refer to this process as data wrangling.
In this Data Science - Wrangling Certificate, which is part of Data Science Professional Certificate from EdX in partnership with Harvard University - HarvardX, we will cover several common steps of the data wrangling process including importing data into R from files, 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 data scientist will likely face them all at some point.
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