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It ensures that the data used in analysis or modeling is comprehensive and comprehensive. Integration also helps avoid duplication and redundancy of data, providing a comprehensive view of the information. EDA provides insights into the data distribution and informs the selection of appropriate preprocessing techniques.
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A Data Scientist requires to be able to visualize quickly the data before creating the model and Tableau is helpful for that. Tableau is useful for summarising the metrics of success. Disadvantages of Tableau for Data Science However, apart from the advantages, Tableau for Data Science also has its own disadvantages.
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Focus on Data Science tools and business intelligence. Practical skills in SQL, Python, and Machine Learning. Focus on exploratoryDataAnalysis and feature engineering. Ideal starting point for aspiring Data Scientists. Hands-on experience through a 1-month internship.
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