Probability and Statistics for Data Science Math + R + Data
Probability and Statistics for Data Science Math + R + Data by Norman S. Matloff, published by CRC Press, Taylor & Francis Group in 2019, spans 412 pages and is presented in English. This book addresses key concepts in probability and statistics, emphasizing practical applications in data science. It incorporates real datasets and R coding to support data analysis, covering topics such as distributions, expected value, and estimation, while also exploring advanced applications like PCA, random graph models, and neural networks.
Readers will find a focus on critical thinking regarding the “how” and “why” of statistics, with a mathematically precise presentation of concepts and models rather than a theorem/proof approach. Prerequisites include calculus, some matrix algebra, and programming experience, making it suitable for those looking to deepen their understanding of statistics within the context of data science and analytics. The book aims to provide a comprehensive foundation for students and professionals interested in the intersection of mathematics, statistics, and data science.
Official synopsis Publisher
Probability and Statistics for Data Science: Math + R + Data covers “math stat”–distributions, expected value, estimation etc.–but takes the phrase “Data Science” in the title quite seriously:
* Real datasets are used extensively.
* All data analysis is supported by R coding.
* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.
* Leads the student to think critically about the “how” and “why” of statistics, and to “see the big picture.”
* Not “theorem/proof”-oriented, but concepts and models are stated in a mathematically precise manner.
Prerequisites are calculus, some matrix algebra, and some experience in programming.
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university’s Distinguished Teaching Award.
Publisher
Topics
FAQ
What is “Probability and Statistics for Data Science Math + R + Data” about?
Who is the author of “Probability and Statistics for Data Science Math + R + Data”?
When was “Probability and Statistics for Data Science Math + R + Data” published?
What is the ISBN for “Probability and Statistics for Data Science Math + R + Data”?
What are the book details (language, pages, edition)?
