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Data wrangling is the process of organizing and cleaning up complex and disorganized data sets so they are simpler to access and analyze.

It is becoming more and more important to organize large amounts of available data for analysis as data and data sources are rapidly growing and expanding. To make the data easier to use and organize, this process typically entails manually converting and mapping data from one raw form into another.

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The Purposes of Data Wrangling

• assemble information from various sources to reveal a “deeper intelligence”
• Put timely access to accurate, usable data in the hands of business analysts.
• Reduce the amount of time needed to collect and organize messy data before using it.
• allowing data scientists and analysts to concentrate on data analysis rather than data wrangling
• Improve the ability of senior leaders in an organization to make decisions.

Important Steps in Data Wrangling

• Data gathering: Locate the data in your sources and gain access to it.
• Combining Data: For additional use and analysis, combine the edited data.
• Data cleaning: Correct or remove any incorrect data and redesign the data into a usable and practical format.