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4 июл 2017 Название: The Data Science Design Manual Автор: Steven S. Skiena Издательство: Springer Год: 2017. Формат: PDF Размер: 21,4 Мб Язык: английский / English This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of
The Data Science Design Manual. 0 The course textbook is my new book, published by Springer-Verlag, 2017. 0 I will appreciate feedback / error correction. 0 Recommended texts include Nate Silver's. “The Signal and the Noise" and “Python for. Data Analysis".
Download PDF (573KB). Chapter. Pages 1-25. What is Data Science? Steven S. Skiena · Download PDF (1687KB). Chapter. Pages 27-56. Mathematical Preliminaries · Steven S. Skiena · Download PDF (1022KB). Chapter. Pages 57-93. Data Munging · Steven S. Skiena · Download PDF (1189KB). Chapter. Pages 95-120.
analyzing and interpreting data requires both technical expertise and wisdom. That so many people do these basics so badly provides my inspiration for writing this book. To the Reader. I have been gratified by the warm reception that my book The Algorithm Design. Manual [Ski08] has received since its initial publication in
Data-science-design-manual-notebooks - Figure notebooks for "The Data Science Design Manual" (www.data-manual.com/)
The Data Science Design Manual serves as an introduction to data science, focusing on the skills and principles needed to build systems for collection, analyzing, and interpreting data. As a discipline data science sits at the intersection of statistics, computer science, and machine learning, but it is building a distinct heft and
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The Data Science Design Manual (Texts in Computer Science) [Steven S. Skiena] on Amazon.com. *FREE* shipping on qualifying offers. This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles
Provides an introduction to data science, focusing on the fundamental skills and principles needed to build systems for collecting, analyzing, and.
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