Data Munging (also known as Data Wrangling, Data Preparation or Data Manipulation) is the process of cleaning and restructuring large data sets to make them more useful and understandable. It involves sorting and selecting data, converting data types, consolidating data, merging datasets, removing missing values, and more.
Data Munging can be applied to any type of data, including text, numbers, images, videos, and other digital information. It is often used when dealing with messy or incomplete data sets. For example, a data munging process might involve combining several data sources into one meaningful dataset, or filling in missing values with appropriate estimates. The goal of data munging is to improve the quality of data so it can be used more effectively in business intelligence, analytics, and data mining.
Data Munging is an essential step in the data science process. It is often the first step in a data science project, as it sets the stage for the rest of the data analysis by transforming the data into a more usable and quantifiable form.
Data munging is a difficult task, as it requires a lot of manual labor. In most cases, data munging is done manually by a team of data scientists, but there are numerous automated tools and scripts available which can significantly simplify and speed up the process. In addition, data munging may require additional software, such as statistical packages, and a certain degree of technical knowledge.
