Data Science: Wrangling

MOOC
Data Science: Wrangling
Language
English
Duration
5 months
Certificate
Certification paid
Course by EdX
Data Science: Wrangling
What will you learn?
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 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.

Program
Data Science: Wrangling
Learn to process and convert raw data into formats needed for analysis.
Lecturers
Rafael Irizarry
Rafael Irizarry
Professor of Biostatistics Harvard University
Platform
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All the courses on this platform are free of charge. The authors are top universities and corporations that seek to maintain high quality standards. If you do not meet a deadline for assignments, you lose points. Like on other platforms, the videos in which the theory is explained are followed by practical assignments. Courses are available in English, Chinese, Spanish, French and Hindi.
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