Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python [Book]
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Why exploratory data analysis is a key preliminary step in data science||How random sampling can reduce bias and yield a higher-quality dataset, even with big data||How the principles of experimental design yield definitive answers to questions||How to use regression to estimate outcomes and detect anomalies||Key classification techniques for predicting which categories a record belongs to||Why exploratory data analysis is a key preliminary step in data science||How random sampling can reduce bias and yield a higher-quality dataset, even with big data||How the principles of experimental design yield definitive answers to questions||How to use regression to estimate outcomes and detect anomalies||Key classification techniques for predicting which categories a record belongs to||Statistical machine learning methods that "learn" from data||Unsupervised learning methods for extracting meaning from unlabeled data||About the Author