Analyzing the right data is crucial for an analytics project’s success. Most of the times, data from transactional systems or other data sources such as surveys, social media, and sensors are not ready to be analyzed directly. Data has to mix and matched, massaged and preprocessed to transform it into a proper form which can be analyzed. Without this, the data being analyzed and reported on becomes meaningless. And these small discrepancies can make a significant difference in the outcomes that can affect an organization’s bottom line performance.
With R being one of the most preferred tools for Data Science and Machine Learning, we’ll discuss some data management techniques using it.
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