Data Science: Wrangling

Provided by Harvard University (HarvardX)
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Introductory
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Learn to process and convert raw data into formats needed for analysis.

Part of 1 program:
Course Format:Instructor-Led
Start Date:Mar 20, 2019

What you will learn

  • Importing data into R from different file formats
  • Web scraping
  • How to tidy data using the tidyverse to better facilitate analysis
  • String processing with regular expressions (regex)
  • Wrangling data using dplyr
  • How to work with dates and times as file formats
  • Text mining

Overview

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.

Before you start

  • No prerequisites
  • Instructor-Led: course contains assignments and exams that have specific due dates, and you complete the course within a defined time period.
  • Course ends: Mar 20, 2019

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