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I found some information about Data Wrangling and they say different things.

In this one, they say data cleaning is a subcategory of data wrangling link

In this PDF, data wrangling is EDA and model building link

Can someone explain to me what data wrangling is? What is the difference between data cleaning and data wrangling? If possible can you add the reference?

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Data Wrangling

Data wrangling also referred to as data munging, is the process of converting and mapping data from one raw format into another. The purpose of this is to prepare the data in a way that makes it accessible for effective use further down the line. Not all data is created equal, therefore it’s important to organize and transform your data in a way that can be easily accessed by others.

Data Cleaning

Data cleaning, also referred to as data cleansing, is the process of finding and correcting inaccurate data from a particular data set or data source. The primary goal is to identify and remove inconsistencies without deleting the necessary data to produce insights. It’s important to remove these inconsistencies in order to increase the validity of the data set.

Cleaning encompasses a multitude of activities such as identifying duplicate records, filling empty fields and fixing structural errors. These tasks are crucial for ensuring the quality of data is accurate, complete, and consistent. Cleaning assists in fewer errors and complications further downstream.


Data cleaning vs Data-wrangling

  • Data cleaning focuses on removing erroneous data from your data set. In contrast, data-wrangling focuses on changing the data format by translating "raw" data into a more usable form.

  • Data cleaning improves the correctness and consistency of the data, whereas data-wrangling prepares the data structurally for modeling.

It's crucial to remember that data wrangling may be time-consuming and resource-intensive, especially when done manually. For a firm that wishes to benefit from the best and most result-driven BI and analytics, data wrangling is a crucial component of the process.

Most people think that your insights and analyses are only as good as the data you're using while working with data. Data cleansing is used frequently by organisations that collect data directly from consumers via surveys, questionnaires, and forms. In their case, this means double-checking that data was entered into the correct field, that no invalid characters were included, and that the information provided was accurate.

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