Brief Introduction
Learn to process and convert raw data into formats needed for analysis.
Description
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.
Knowledge
- 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
Data Wrangling with Python
- 3.6
-
![]()
![]()
![]()
![]()
![]()
Creating actionable data from raw sources For data to be useful and meaningful, it must be curated and refined. About the Author...
R for Beginners
- 4.6
-
![]()
![]()
![]()
![]()
![]()
Introduction to Programming Language R. Tools for Data Science, Data Analysis and Statistical Analysis. RStudio. Do you use spreadsheets for data cleaning, wrangling, visualization, and data analysis?...
PySpark Essentials for Data Scientists (Big Data + Python)
- 4.5
-
![]()
![]()
![]()
![]()
![]()
Learn how to wrangle Big Data for Machine Learning using Python in PySpark taught by an industry expert! Use Python with Big Data on a distributed framework (Apache Spark)...
Modeling Data in the Tidyverse
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Modeling Data Basics...
