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Data Science: Wrangling

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

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Data Science: Wrangling

There is one session available:

84,931 already enrolled!
Starts Nov 21

Data Science: Wrangling

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

Data Science: Wrangling
8 weeks
1–2 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

84,931 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Nov 21

About this course

Skip About this course

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.

At a glance

  • Language: English
  • Video Transcript: English
  • Associated programs:

What you'll learn

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  • 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

About the instructors

Frequently Asked Questions

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