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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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Resiliency: Resiliency in Data Science refers to the ability of a system to recover from failures and continue to function. It involves designing systems that can withstand unexpected events, such as hardware failures, network outages, or cyber attacks ... Read More >>
Outer Joins: In data science, outer join is a type of join operation that combines data from two or more tables based on a common attribute and includes all the rows from one or both tables, even if there is no match in the other table. Outer joins are ... Read More >>
Collaborations: Collaborations in data science refer to the process of working together with other individuals or teams to achieve a common goal. This involves sharing data, knowledge, and expertise to develop solutions to complex problems. Collaborations ... Read More >>

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