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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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Programming Model: Programming Model refers to the style of programming that a developer uses to create software. It includes the set of rules, guidelines, and patterns that are used to design and implement software applications. A programming model can be th ... Read More >>
Unique Identifier: A unique identifier is a value or a set of values that can be assigned to a data entity to uniquely identify it within a given context. In data science, unique identifiers are used to ensure that each data point is distinct and can be easil ... Read More >>
Foreign Key: In Data Science, a foreign key is a field or a set of fields in a table that uniquely identifies a record in another table. It is used to establish a link between two tables and enforce referential integrity between them. The foreign key co ... Read More >>

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