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"Practical Statistics for Data Scientists" is a comprehensive guide that caters to data scientists and those seeking to delve deeper into the world of data science from a statistical perspective. The book serves as a quick reference that bridges the gap between formal statistics training and the practical application of statistical methods to data science problems. With a focus on exploratory data analysis, the book is an essential resource for data scientists who want a more detailed understanding of key concepts and techniques. The authors recognize that basic statistics rarely cover the full scope of what data scientists need, and many data science resources do not adequately incorporate statistical methods. In response, they have compiled 50 essential concepts and provided a wealth of examples, code snippets, and practical guidance to help data scientists better understand and apply these statistical concepts. The book covers topics such as random sampling, data distrib

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  Introduction to Algorithms fourth Edition is a comprehensive and authoritative textbook on computer algorithms, suitable for use in undergraduate and graduate level courses. The book covers a broad range of algorithms, including data structures, graphs, online algorithms, machine learning, and more. The book is written by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, all of whom are prominent scholars in the field of computer science. One of the unique features of Introduction to Algorithms is how it combines rigor and comprehensiveness. The book is designed to be accessible to readers with different backgrounds, yet still provide a thorough treatment of the subject matter. The book also has self-contained chapters, making it easy for readers to focus on specific topics of interest. The introduction to algorithms fourth edition is a renowned textbook that algorithms uniquely combines rigor with analysis accessible. This fourth edition new chapters t

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