Data wrangling involves transforming raw data into usable formats from "summary" of Data Science For Dummies by Lillian Pierson
Data wrangling is a crucial step in the data science process that involves taking raw data and converting it into a format that is more easily usable for analysis. Raw data, straight from its source, often comes in a messy and unstructured form, making it difficult to work with. This raw data may contain missing values, errors, inconsistencies, or other issues that need to be addressed before any meaningful analysis can take place. During the data wrangling process, data scientists clean and preprocess the raw data to make it suitable for analysis. This can involve a variety of tasks, such as removing duplicates, handling missing values, correcting errors, standardizing formats, and transforming data into a more organized structure. By cleaning and preprocessing the data, data scientists can ensure that the data is accurate, complete, and ready for analysis. One of the key goals of data wrangling is to ensure that the data is in a format that can be easily manipulated and analyzed using statistical and machine learning techniques. This often involves transforming the data into a structured format, such as a table or matrix, that can be easily processed by data analysis tools and algorithms. By organizing the data in this way, data scientists can more effectively explore patterns, trends, and relationships within the data. Data wrangling is a time-consuming and labor-intensive process, but it is essential for ensuring the quality and reliability of the data used in data analysis. Without proper data wrangling, data scientists run the risk of drawing incorrect conclusions or making faulty predictions based on flawed or incomplete data. By investing time and effort in data wrangling, data scientists can enhance the quality of their analyses and make more informed decisions based on reliable data.
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