Data Wrangling
Data wrangling, also known as data cleaning or munging, involves transforming and mapping raw data into more usable formats. This competency is critical because data often comes from various sources and may not be in a form suitable for analysis. Data wrangling includes dealing with missing values, correcting errors, and standardizing formatting.
Data scientists spend a significant amount of time in the data wrangling phase, ensuring that the data is accurate and well-prepared for analysis. This step is crucial because the quality of data directly affects the accuracy of the analysis and predictions. Good data wrangling practices lead to reliable data insights, which are essential for any analytical project.
Our repository contains an array of materials which are designed to train and develop data wrangling skills.
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