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Data wrangling is the process of cleaning, transforming, and mapping raw data into a more suitable format for analysis. It involves identifying and correcting errors in the data, handling missing or incomplete data, and converting data into a standardized format. Data wrangling is an important step in the data analysis process as it ensures that the data is accurate, consistent, and complete. It also helps to make the data more accessible and easier to work with, which can save time and improve the accuracy of the analysis. Data wrangling can be a time-consuming and complex process, but it is essential for ensuring that the data is of high quality and can be used effectively for analysis.

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Deposition: Deposition is the process of extracting knowledge or insights from data by transforming, cleaning, and structuring it for analysis. It involves the conversion of raw data into a more usable format, such as a relational database or a data cu ... Read More >>
Interprocess Communication: Interprocess Communication (IPC) refers to the mechanisms and techniques used by operating systems to allow processes to communicate with each other. In the context of Data Science and Artificial Intelligence, IPC is essential for enabling ... Read More >>
Algebraic Structures: In Data Science and Artificial Intelligence, Algebraic Structures refer to a set of mathematical operations that are used to manipulate data and solve problems. These structures include groups, rings, fields, and vector spaces, which are us ... Read More >>

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