Probability and Statistics for Data Science: Math + R + Data by Norman Matloff (

Description: Probability and Statistics for Data Science by Norman Matloff This text is designed for a one-semester junior/senior/graduate-level calculus-based course on probability and statistics, aimed specifically at data science students (including computer science). In addition to calculus, the text assumes basic knowledge of matrix algebra and rudimentary computer programming. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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 universitys Distinguished Teaching Award. Author Biography 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 universitys Distinguished Teaching Award. Table of Contents 1. Basic Probability Models. 2. Discrete Random Variables. 3. Discrete Parametric Distribution Families. 4. Introduction to Discrete Markov Chains. 5. Continuous Probability Models. 6. The Family of Normal Distributions. 7. The Family of Exponential Distributions. 8. Random Vectors and Multivariate Distributions. 9. Statistics: Prologue. 10. Introduction to Confidence Intervals. 11. Introduction to Significance Tests. 12. General Statistical Estimation and Inference 13. Predictive Modeling Review "I quite like this book. I believe that the book describes itself quite well when it says: Mathematically correct yet highly intuitive…This book would be great for a class that one takes before one takes my statistical learning class. I often run into beginning graduate Data Science students whose background is not math (e.g., CS or Business) and they are not ready…The book fills an important niche, in that it provides a self-contained introduction to material that is useful for a higher-level statistical learning course. I think that it compares well with competing books, particularly in that it takes a more "Data Science" and "example driven" approach than more classical books."~Randy Paffenroth, Worchester Polytechnic Institute"This text by Matloff (Univ. of California, Davis) affords an excellent introduction to statistics for the data science student…Its examples are often drawn from data science applications such as hidden Markov models and remote sensing, to name a few… All the models and concepts are explained well in precise mathematical terms (not presented as formal proofs), to help students gain an intuitive understanding."~CHOICE Review Quote "I quite like this book. I believe that the book describes itself quite well when it says: Mathematically correct yet highly intuitive...This book would be great for a class that one takes before one takes my statistical learning class. I often run into beginning graduate Data Science students whose background is not math (e.g., CS or Business) and they are not ready...The book fills an important niche, in that it provides a self-contained introduction to material that is useful for a higher-level statistical learning course. I think that it compares well with competing books, particularly in that it takes a more "Data Science" and "example driven" approach than more classical books."~Randy Paffenroth, Worchester Polytechnic Institute Details ISBN1138393290 Year 2019 ISBN-10 1138393290 ISBN-13 9781138393295 Imprint CRC Press Subtitle Math + R + Data Place of Publication London Country of Publication United Kingdom Author Norman Matloff Publisher Taylor & Francis Ltd DEWEY 519.5 Series Chapman & Hall/CRC Data Science Series Pages 412 Publication Date 2019-06-20 Language English UK Release Date 2019-06-20 AU Release Date 2019-06-20 NZ Release Date 2019-06-20 Format Paperback Alternative 9780367260934 Audience Tertiary & Higher Education We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:134456963;

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