What is the term for the process of cleaning and organizing raw data into a usable format?

The process of cleaning and organizing raw data into a usable format is commonly referred to as data wrangling. This term encompasses a variety of techniques for transforming and mapping data from one raw form into another format that is more beneficial for analysis. Data wrangling typically involves tasks like correcting inaccuracies, dealing with missing values, and restructuring data to fit the needs of the analysis.

Data wrangling is crucial in the data preparation stage because it ensures that data is both accurate and formatted correctly before it is used in further analysis or modeling. This process allows analysts and data scientists to apply various analytical techniques effectively, ensuring that the models they build are based on clean and reliable data.

While the other terms such as data munging, data preprocessing, and data preparation may seem similar and sometimes overlap in meaning, they can carry different nuances or may refer to specific subsets of tasks within the broader scope of data wrangling. For instance, data munging often specifically refers to the act of transforming and cleaning data from its raw form. Developing a clear understanding of these definitions will help you better engage with data science tasks and methodologies.