Manually inspecting data quality and cleaning data is a painful and time-consuming process that can take a huge chunk of a data scientist’s time on a project. According to a 2020 survey of data scientists conducted by Anaconda, data scientists spend approximately 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and visualizing data (21%). Amazon SageMaker offers a range of data preparation tools to meet different customer needs and preferences. For users who prefer a GUI-based interactive interface, SageMaker Data Wrangler offers 300+ built-in visualizations, analyses, and transformations to efficiently process data backed by Spark without writing a single line of code.
Data visualization in machine learning (ML) is an iterative process and requires continuous visualization of the dataset for discovery, investigation and validation. Putting data into perspective entails seeing each of the columns to comprehend possible data errors, missing values, wrong data types, misleading/incorrect data,

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