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Probability and Statistics for Data Science Norman Matloff

Probability and Statistics for Data Science von Norman Matloff

Probability and Statistics for Data Science Norman Matloff

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Zusammenfassung

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.

Probability and Statistics for Data Science Zusammenfassung

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

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.

Probability and Statistics for Data Science Bewertungen

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

Über Norman Matloff

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.

Inhaltsverzeichnis

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

Zusätzliche Informationen

GOR011912052
9781138393295
1138393290
Probability and Statistics for Data Science: Math + R + Data Norman Matloff
Norman Matloff
Chapman & Hall/CRC Data Science Series
Gebraucht - Sehr Gut
Broschiert
Taylor & Francis Ltd
2019-06-20
412
N/A
Die Abbildung des Buches dient nur Illustrationszwecken, die tatsächliche Bindung, das Cover und die Auflage können sich davon unterscheiden.
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