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HarvardX: Data Science: Wrangling

4.3 stars
18 ratings

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
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Starts Mar 19
Ends Jun 19
Starts Apr 17
Ends Dec 18

About this course

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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:
  • Associated skills:Data Science, Web Pages, Text Mining, HyperText Markup Language (HTML), Data Wrangling, Parsing

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

Frequently Asked Questions

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This course is part of Data Science Professional Certificate Program

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Expert instruction
9 skill-building courses
Self-paced
Progress at your own speed
1 year 5 months
2 - 3 hours per week

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